Versione Finale.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "972_e_975_e_snippet_cross_validation.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "kaPNdsmPJI7E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "d47d70d3-2970-440c-a92e-7d99b9883957"
      },
      "source": [
        "import numpy as np\n",
        "from collections import Counter\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import nltk\n",
        "import re\n",
        "import requests\n",
        "import csv\n",
        "from sklearn import svm\n",
        "import zipfile\n",
        "from wordcloud import WordCloud\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from nltk.stem.snowball import ItalianStemmer\n",
        "! spacy download it\n",
        "#SpaCy is a free open-source library for Natural Language Processing\n",
        "import spacy\n",
        "#import an available pretrained statistical models for Italian\n",
        "import it_core_news_sm\n",
        "#Calling the nlp object on a string of text will return a processed Doc\n",
        "nlp = it_core_news_sm.load()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting it_core_news_sm==2.1.0\n",
            "\u001b[?25l  Downloading https://github.com/explosion/spacy-models/releases/download/it_core_news_sm-2.1.0/it_core_news_sm-2.1.0.tar.gz (11.1MB)\n",
            "\u001b[K     |████████████████████████████████| 11.1MB 829kB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: it-core-news-sm\n",
            "  Building wheel for it-core-news-sm (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for it-core-news-sm: filename=it_core_news_sm-2.1.0-cp36-none-any.whl size=11123297 sha256=61fbbddbe11e3f45e6298daa7b53e46428b653e465e96cea7c2a317003cb1d92\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-k1yxg_up/wheels/8a/7b/11/1010c25163c5166f22f68205123898a33cbe535e0f7525a9ab\n",
            "Successfully built it-core-news-sm\n",
            "Installing collected packages: it-core-news-sm\n",
            "Successfully installed it-core-news-sm-2.1.0\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the model via spacy.load('it_core_news_sm')\n",
            "\u001b[38;5;2m✔ Linking successful\u001b[0m\n",
            "/usr/local/lib/python3.6/dist-packages/it_core_news_sm -->\n",
            "/usr/local/lib/python3.6/dist-packages/spacy/data/it\n",
            "You can now load the model via spacy.load('it')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sBCtqErWNGG9",
        "colab_type": "text"
      },
      "source": [
        "DO STOPWORDS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kF7Axzq0KgJs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "request=requests.get('https://gist.githubusercontent.com/enryold/0676352e48788a24fe792e66b7e99e3c/raw/a182501d03bf1b52001706f932a72606a443c50a/Italian%2520woman%2520names', allow_redirects=True)\n",
        "open('femalepropernames.txt', 'wb').write(request.content)    \n",
        "\n",
        "request=requests.get('https://raw.githubusercontent.com/Alir3z4/stop-words/master/italian.txt')\n",
        "open('stopwords.txt', 'wb').write(request.content)\n",
        "\n",
        "request=requests.get('https://gist.githubusercontent.com/marciuz/5874431/raw/65d9dfaadcb8d68aecf8ff48f0e2683b06600737/first_name_ITA_M.txt', allow_redirects=True)\n",
        "open('malepropernames.txt', 'wb').write(request.content) \n",
        "\n",
        "SW = open('stopwords.txt', 'r', encoding='utf-8').read().lower().splitlines()\n",
        "SW+=('lunedi lunedì martedì martedi mercoledi mercoledì giovedi giovedì venerdi venerdì sabato domenica gennaio febbraio marzo aprile maggio giugno luglio agosto settembre ottobre novembre dicembre').split()\n",
        "SW += open('malepropernames.txt', 'r', encoding='utf-8').read().lower().splitlines()\n",
        "SW += open('femalepropernames.txt', 'r', encoding='utf-8').read().lower().splitlines()\n",
        "\n",
        "#Rimuovo il \"non\"\n",
        "SW.remove('non')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NgkW0FHzVr1n",
        "colab_type": "text"
      },
      "source": [
        "DATA LOADING\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "enGv1oBLVfXG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "request=requests.get('http://dbdmg.polito.it/wordpress/wp-content/uploads/2020/01/dataset_winter_2020.zip')\n",
        "open('dataset_winter_2020.zip', 'wb').write(request.content)\n",
        "with zipfile.ZipFile('dataset_winter_2020.zip', 'r') as zip_fol:\n",
        "    zip_fol.extractall('.')\n",
        "\n",
        "labelled_df=pd.read_csv('./development.csv')\n",
        "number_of_labelled=len(labelled_df)\n",
        "evaluation_df=pd.read_csv('./evaluation.csv')\n",
        "total_df=labelled_df.append(evaluation_df, sort=False).reset_index().drop(columns='index')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aeWRMgQO3r5t",
        "colab_type": "text"
      },
      "source": [
        "Qualche statistica sui dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2s6LVo9Vw3X",
        "colab_type": "code",
        "outputId": "33081826-1bae-4a26-daaf-cfb074c58a2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "num_pos_reviews=(labelled_df['class']=='pos').sum()\n",
        "num_neg_reviews=(labelled_df['class']=='neg').sum()\n",
        "print(f\"Numero totale di positive reviews: {num_pos_reviews}\")\n",
        "print(f\"Numero totale di negative reviews: {num_neg_reviews}\")\n",
        "\n",
        "#se ho una series e ci faccio .str, vedo ogni elemento della serie come una stringa\n",
        "#qui ritorna una lista di boolean a secondo che ci sia quella espressione o no,\n",
        "#e poi la sommmo cosi ho IL NUMERO DI REVIEWS CHE LA CONTENGONO, NON IL NUMERO TOT DI VOLTE IN CUI COMPARE IN TUTTE LE REVIEW\n",
        "evaluation_df['text'].str.contains('luglio',regex=False).sum()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Numero totale di positive reviews: 19532\n",
            "Numero totale di negative reviews: 9222\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "140"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UOMuo6D03VtF",
        "colab_type": "code",
        "outputId": "70697baa-2a09-447a-9bec-d2f837dd9c5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        }
      },
      "source": [
        "#calcola lunghezza in words di ogni review (in questo caso del labelled_df)\n",
        "vettore_lunghezze=[]\n",
        "for review in labelled_df['text'].tolist():\n",
        "  parole=review.split()\n",
        "  vettore_lunghezze.append(len(parole))\n",
        "plt.hist(vettore_lunghezze, 500, facecolor='blue',alpha=0.5)\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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M32jWmC00heSRhKRxMhTeYPzjPZLGyemjY8BSQWF4SDpahsIb3JE3/P6pJUNA0qBWPBSS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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mK5kJFcd4q2_",
        "colab_type": "code",
        "outputId": "def11158-1e8b-40d0-c00d-4d78434f9763",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "#qui dell'evaluation\n",
        "vettore_lunghezze=[]\n",
        "for review in evaluation_df['text'].tolist():\n",
        "  parole=review.split()\n",
        "  vettore_lunghezze.append(len(parole))\n",
        "plt.hist(vettore_lunghezze, 500, facecolor='blue',alpha=0.5)\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EiA0Gqi8GxlS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "67497bac-eb99-4804-db2e-1e2da0f57fe5"
      },
      "source": [
        "labelled_df['num_words'] = labelled_df['text'].apply(lambda x: len(x.split()))\n",
        "labelled_df"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>class</th>\n",
              "      <th>num_words</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Non è l'hotel più lussuoso in cui abbia mai so...</td>\n",
              "      <td>pos</td>\n",
              "      <td>715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Siamo stati qui per 1 notte prima della nostra...</td>\n",
              "      <td>pos</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hotel è ben posizionato per visitare Torino. A...</td>\n",
              "      <td>pos</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>All'arrivo la cordialità e disponibilità dello...</td>\n",
              "      <td>pos</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Abbiamo soggiornato per due notti alla fine de...</td>\n",
              "      <td>pos</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28749</th>\n",
              "      <td>L'hotel è vecchio ma caratteristico e devo dir...</td>\n",
              "      <td>neg</td>\n",
              "      <td>152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28750</th>\n",
              "      <td>Per essere un 4 stelle L la camera era un pò s...</td>\n",
              "      <td>pos</td>\n",
              "      <td>117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28751</th>\n",
              "      <td>Io e mia mamma (di età compresa tra 23 e 62) s...</td>\n",
              "      <td>pos</td>\n",
              "      <td>272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28752</th>\n",
              "      <td>Ci siamo sentiti accolti e coccolati fin dall'...</td>\n",
              "      <td>pos</td>\n",
              "      <td>106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28753</th>\n",
              "      <td>Soggiorno fantastico in una posizione fantasti...</td>\n",
              "      <td>pos</td>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>28754 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    text class  num_words\n",
              "0      Non è l'hotel più lussuoso in cui abbia mai so...   pos        715\n",
              "1      Siamo stati qui per 1 notte prima della nostra...   pos         49\n",
              "2      Hotel è ben posizionato per visitare Torino. A...   pos         58\n",
              "3      All'arrivo la cordialità e disponibilità dello...   pos         36\n",
              "4      Abbiamo soggiornato per due notti alla fine de...   pos         62\n",
              "...                                                  ...   ...        ...\n",
              "28749  L'hotel è vecchio ma caratteristico e devo dir...   neg        152\n",
              "28750  Per essere un 4 stelle L la camera era un pò s...   pos        117\n",
              "28751  Io e mia mamma (di età compresa tra 23 e 62) s...   pos        272\n",
              "28752  Ci siamo sentiti accolti e coccolati fin dall'...   pos        106\n",
              "28753  Soggiorno fantastico in una posizione fantasti...   pos         56\n",
              "\n",
              "[28754 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wgQaNQMgF7SO",
        "colab_type": "text"
      },
      "source": [
        "LUNGHEZZA PAROLE E POS O NEG DELLA REVIEW NEL DEVELOPMENT SET"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8KDFIsEGF-wP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "73fd643d-0f78-4f68-9f73-95ced044eea4"
      },
      "source": [
        "import plotly.graph_objs as go\n",
        "from plotly.tools import FigureFactory as FF\n",
        "from plotly.offline import iplot\n",
        "\n",
        "x1 = labelled_df.loc[labelled_df['class'] == 'neg', 'num_words']\n",
        "x0 = labelled_df.loc[labelled_df['class'] == 'pos', 'num_words']\n",
        "\n",
        "trace1 = go.Histogram(\n",
        "    x=x0, name='Pos',\n",
        "    opacity=0.75\n",
        ")\n",
        "trace2 = go.Histogram(\n",
        "    x=x1, name = 'Neg',\n",
        "    opacity=0.75\n",
        ")\n",
        "\n",
        "data = [trace1, trace2]\n",
        "layout = go.Layout(barmode = 'group', title='Distribution of Review Lengths Based on Recommendation')\n",
        "fig = go.Figure(data=data, layout=layout)\n",
        "\n",
        "iplot(fig, filename='stacked histogram')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                    Plotly.newPlot(\n",
              "                        'ccb803a4-a5da-44aa-bcc0-fa45225baf7a',\n",
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GgzYSgNaC0ti",
        "colab_type": "code",
        "outputId": "d153cf32-2cd8-4a25-8e2f-6bac0292c2f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "#DA AGGIUNGERE COME FEATURES??\n",
        "#statistiche sul labelled dataset\n",
        "for word in ['\\w![^!]','\\?','!\\?','\\?!','[^!]!![^!]','!{3}','\\?\\?', '\\?{3}']:\n",
        "    print(word, (np.array(total_df['class'][:number_of_labelled]  == 'pos') & np.array(total_df['text'][:number_of_labelled].str.contains(word, regex=True))).sum() / np.array(total_df['text'][:number_of_labelled].str.contains(word, regex=True)).sum() * 100, \"nelle positive\")\n",
        "    print(word, (np.array(total_df['text'][:number_of_labelled].str.contains(word, regex=True))).sum()/number_of_labelled*100, 'nelle labelled')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\\w![^!] 66.26086956521739 nelle positive\n",
            "\\w![^!] 21.996939556235652 nelle labelled\n",
            "\\? 33.82352941176471 nelle positive\n",
            "\\? 6.148709744731168 nelle labelled\n",
            "!\\? 12.048192771084338 nelle positive\n",
            "!\\? 0.28865549140989083 nelle labelled\n",
            "\\?! 22.5 nelle positive\n",
            "\\?! 0.5564443207901509 nelle labelled\n",
            "[^!]!![^!] 51.792114695340494 nelle positive\n",
            "[^!]!![^!] 3.881199137511303 nelle labelled\n",
            "!{3} 49.46949602122016 nelle positive\n",
            "!{3} 7.8667315851707595 nelle labelled\n",
            "\\?\\? 16.716417910447763 nelle positive\n",
            "\\?\\? 1.1650552966543786 nelle labelled\n",
            "\\?{3} 13.636363636363635 nelle positive\n",
            "\\?{3} 0.7651109410864576 nelle labelled\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EwCwWkj6O6oM",
        "colab_type": "code",
        "outputId": "5eb6e657-045c-46c2-be6a-cf85f22da160",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "\n",
        "X = np.array([1,3,5]) #! !! !!!\n",
        "Y = [0.66,0.51,0.49] #pos\n",
        "Z = [0.34,0.49,0.51] #neg\n",
        "\n",
        "plt.bar(X - 0.4, Y, label='pos') # offset of -0.4\n",
        "plt.bar(X + 0.4, Z, label='neg') # offset of  0.4\n",
        "plt.legend(loc=(1.1,0.5))\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AACSgMAEASJBUmLabbHfa7rK9eJDtH7b9Y9u9tq/LPiYAAPkasjBtV0taLulqSQ2SbrDd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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "04jo-1SMTHsK",
        "colab_type": "code",
        "outputId": "9aeb75c2-bf17-4085-e48e-a7ea2551b9bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "X = np.array([1,3,5]) #? ?? ???\n",
        "Y = [0.33,0.16,0.13] #pos\n",
        "Z = [0.67,0.84,0.87] #neg\n",
        "\n",
        "plt.bar(X - 0.4, Y, label='pos') # offset of -0.4\n",
        "plt.bar(X + 0.4, Z, label='neg') # offset of  0.4\n",
        "plt.legend(loc=(1.1,0.5))\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pscLtZyRdYdvZjQkAQL6qE9ZMkLSr6P5uSb95uDUR0W17r6RTJP2yeJHt+ZLmF+7us91a\n9PT4vusrhcvpvX13UH/PKZ/3NQhl9ecl8Wem4+7PbFK2wyBVSjAzExHLJC3r7znbzRHReCznOVYq\n9b3xvspPpb63Sn1fUmW/t3KTckh2j6SJRfdrC4/1u8Z2taRxkt7NYkAAAEpBSjDXS6q3Pdn2cElz\nJa3ps2aNpD8o3L5W0o8jIrIbEwCAfA14SLbwmeQCSeskDZO0IiJabC+W1BwRayQ9Kun7ttskvafe\nqA5Wv4dqK0SlvjfeV/mp1PdWqe9Lquz3VlbMjiAAAAPjSj8AACQgmAAAJCiJYA506b1yZXuF7U7b\nm/OeJUu2J9p+0fYW2y2278p7pizYHmn7P23/rPC+vpv3TFmyPcz2RtvP5j1Llmy3237T9ibbzXnP\nkxXbJ9l+xvY221ttX5z3TMe73D/DLFx6b7ukr6v3ogjrJV0fEVtyHSwDtr8qaZ+kVRFxbt7zZMX2\n6ZJOj4if2h4jaYOk3y33P7PC1alOjIh9tk+Q9IqkuyLi9ZxHy4TtuyU1ShobEVfnPU9WbLdLaoyI\n8rlwQQLbj0l6OSKWF76hMCoifpX3XMezUtjDTLn0XlmKiJ+o96zhihIRb0fETwu3P5S0Vb1Xeypr\n0Wtf4e4JhX8q4qw427WSvilped6zYGC2x0n6qnq/gaCIOEAs81cKwezv0ntl/3++x4vCL9NMl/RG\nvpNko3DYcpOkTkn/FhEV8b4k/Y2kP5XUk/cgR0FIet72hsLlNyvBZEldkv6+cBh9ue0T8x7qeFcK\nwUSZsj1a0g8l/XFEfJD3PFmIiIMRcYF6r2h1ke2yP5Ru+2pJnRGxIe9ZjpLfjogL1fuLSn9U+Cik\n3FVLulDS30bEdEkfSaqY8zvKVSkEM+XSeygxhc/4fijp8Yj4p7znyVrh8NeLkpryniUDMyXNKnzW\n96Sky23/Q74jZSci9hT+3SnpR+r9mKfc7Za0u+gIxzPqDShyVArBTLn0HkpI4eSYRyVtjYjv5T1P\nVmzX2D6pcPtL6j0RbVu+Ux25iPhORNRGRJ16//v6cUTcmPNYmbB9YuHEMxUOWV4pqezPSo+IDkm7\nbJ9deOgKSWV9Ul0lOKa/VtKfw116L+exMmH7CUmXSRpve7ekv4iIR/OdKhMzJd0k6c3C532S9GcR\nsTbHmbJwuqTHCmduV0l6OiIq6isYFegrkn5U+Pndakk/iIh/zXekzHxL0uOFHYmdkv4w53mOe7l/\nrQQAgHJQCodkAQAoeQQTAIAEBBMAgAQEEwCABAQTAIAEBBMAgAQEEwCABP8LBUuPfazQzTEAAAAA\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ooF2tyZ16szb",
        "colab_type": "text"
      },
      "source": [
        "ANALIZZO EMOJI EMOTICONS E AGGIUNGO LA LORO FEATURE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ahqmXOX6rPg",
        "colab_type": "code",
        "outputId": "99163cf8-8c57-47b1-9c22-838d26ee5562",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "neg_emojis = '👿🖕😾😡😠😧💔💩😱🙀😭😟👎😰😖😕😢😿😞🤕😨😳☹️😬🤥🤢😣💀☠️🤧😫😒😩😥😵👊😦😯😷😔🙄🙁😓🤔🤐'\n",
        "pos_emojis = '🤝😆🙏🙂😛😎👍😲😊🤠🤞😁😀🤗💋😗😽😚😙👄👌☺️😌😄😸😃😺😏😼😅✌️💯🖤💙👏💘💝💚❤️😍😻💓💗😇😂😹😘💜💞💖💕😉💛😋🙌🤣'\n",
        "pos_emojis_regex='|'.join(list(pos_emojis))\n",
        "pos_emoticons_regex='<3|:-\\)|;\\)|;-\\)|:\\)'\n",
        "neg_emojis_regex='|'.join(list(neg_emojis))\n",
        "neg_emoticons_regex=':\\(|:-\\(|-_-|;\\(|;-\\(|-\\.-'\n",
        "#fai statistica per labelled e per evaluation magari\n",
        "pos_emot_emoji_count = total_df['text'].str.contains(pos_emoticons_regex+'|'+pos_emojis_regex,regex=True).sum()\n",
        "#è il numero di documenti (tra labelled e non) che ha almeno una emoticons o emoji positiva \n",
        "neg_emot_emoji_count = total_df['text'].str.contains(neg_emoticons_regex+'|'+neg_emojis_regex,regex=True).sum()\n",
        "#idem per le negative\n",
        "print(pos_emot_emoji_count)\n",
        "print(neg_emot_emoji_count)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "624\n",
            "162\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ISDHBDE7FMd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#FEATURE SUL DATASET TOTALE\n",
        "total_df['has_pos_emoj_emot'] = total_df['text'].str.contains(pos_emoticons_regex+'|'+pos_emojis_regex,regex=True) \n",
        "total_df['has_neg_emoj_emot'] = total_df['text'].str.contains(neg_emoticons_regex+'|'+neg_emojis_regex,regex=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "feCIi2kA7pCQ",
        "colab_type": "text"
      },
      "source": [
        "STATISTICHE SULLE REVIEWS CON EMOJI EMOTS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cCh1CO0j7M7J",
        "colab_type": "code",
        "outputId": "3213a5b8-5a34-4c2f-80a3-f38ac4e1236b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "arr1=np.array(total_df['has_pos_emoj_emot'][:number_of_labelled] == True) #array con True se hanno emoj_emot pos (tra le labelled)\n",
        "arr2=np.array(total_df['class'][:number_of_labelled] == 'pos') #array con indici True se sono reviews pos (tra le labelled)\n",
        "print(np.array(arr1 & arr2).sum()/arr1.sum())\n",
        "\n",
        "arr1=np.array(total_df['has_neg_emoj_emot'][:number_of_labelled] == True) #array con True se hanno emoj_emot neg (tra le labelled)\n",
        "arr2=np.array(total_df['class'][:number_of_labelled] == 'neg') #array con indici True se sono reviews neg (tra le labelled)\n",
        "print(np.array(arr1 & arr2).sum()/arr1.sum())"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8832951945080092\n",
            "0.6050420168067226\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hwnJxEci-I4h",
        "colab_type": "text"
      },
      "source": [
        "TOKENIZER LEMMATIZER STEMMATIZER"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TDeR54OL73EM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "stemmer = ItalianStemmer()\n",
        "\n",
        "def tokenizer_lemm_stemm(review):\n",
        "    #review è stringa\n",
        "    review=re.sub('[^abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZÀÁÈÉÌÍÒÓÙÚàáèéìíòóùú ]',' ', review)\n",
        "    #ora review è stringa in cui tutti i char che non siano tra quelli scritti(lì ho anche lo spazio) sono sostituiti da uno spazio.\n",
        "    #quindi tolti i numeri e TUTTA la punteggiatura\n",
        "    review=review.lower() #review è una stringa e la converto a lowercase tutta\n",
        "    tokens=[token.strip() for token in review.split() if token not in SW] #tokens è lista di stringhe\n",
        "\n",
        "    doc=nlp(' '.join(tokens)) #doc è una \"striigna speciale\" con la review dopo il processo di prima\n",
        "    tokens=[token.lemma_ for token in doc]\n",
        "    tokens=[stemmer.stem(token) for token in tokens]\n",
        "    return tokens"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SV0wYt5y-00s",
        "colab_type": "text"
      },
      "source": [
        "LO APPLICO ALL'INTERO DATASET"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u0VdBY1Y-3IC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "total_df['text_tokenized_lemmed'] = total_df['text'].apply(tokenizer_lemm_stemm)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tZS1zjD-_Fc3",
        "colab_type": "text"
      },
      "source": [
        "FACCIO WORDCLOUD DELLE REVIEWS LABELLATE DOPO TOKEN LEM STEMM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I_w2BGYj_Ir3",
        "colab_type": "code",
        "outputId": "e2205f2d-8814-4798-d4ee-8283cc57bc4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        }
      },
      "source": [
        "all_tokenized_lemmed_wordscloud = ' '.join([' '.join(review) for review in total_df['text_tokenized_lemmed'][:number_of_labelled]])\n",
        "#stringa con tutte le parole dopo il tokenizer_lemm_stemm, di tutti i documenti labellati (considerati anche i doppioni)\n",
        "plt.imshow(WordCloud(collocations=False, stopwords=[], max_font_size=100, max_words=100, background_color=\"white\", random_state=1).generate(all_tokenized_lemmed_wordscloud), \n",
        "            interpolation='bilinear')"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f9f38bce208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/9sePI4gC+3ddZGIwhMkksW1LC7IBRw/18dwXNuBwKFcVgLoeqio8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VbZsLGBn/nAVhoaonMBXtdN3t7fy49+dIipeJpCwSCZzGMYJpWxANu2tvDvfvVuJieS/NmfPsuO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\ne5BF577IIoss8h7k/wdv2aFu2TMNkwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kzYxjC2eVyBn",
        "colab_type": "text"
      },
      "source": [
        "FACCIO WORDCLOUD DELLE REVIEWS NON LABELLATE DOPO TOKEN LEM STEMM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vwQASJROVz9p",
        "colab_type": "code",
        "outputId": "af4cad91-d3f7-4c15-9c8f-7d677d0c1fae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "all_tokenized_lemmed_wordscloud2 = ' '.join([' '.join(review) for review in total_df['text_tokenized_lemmed'][number_of_labelled:]])\n",
        "print(len(all_tokenized_lemmed_wordscloud))\n",
        "print(len(all_tokenized_lemmed_wordscloud2))\n",
        "\n",
        "plt.imshow(WordCloud(collocations=False, stopwords=[], max_font_size=100, max_words=100, background_color=\"white\", random_state=1).generate(all_tokenized_lemmed_wordscloud2), \n",
        "            interpolation='bilinear')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "11251988\n",
            "4810836\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f9f32793e48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pvPijQ7P0gZ/93F3sfGglFovEubPDfPc7e0mmcqRTOTKZPAf2d3L+/AiKLHHf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BoG2aLBkb6SpI8zJXlwpG8Qv64R0DX8mkbM7+XOFc1UhRc3Hd7WWMeO5jqePXGG8UyOr73yFv/4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3Gn+xj8Rkkv0vHmHg7DD3fXI76+5ZddUuQABZcwQbE6dSQcYYQZX96GhcGGBkWWLd6lo+9fEt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\nJDr3IAiCLuQ2prCk5gMjWzkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mqy4qqXjAPER",
        "colab_type": "text"
      },
      "source": [
        "CERCO LE PAROLE TOKENIZZ LEMMIZZ STEMMIZZ PIU' COMUNI TRA LE LABELLED PERCHE' QUESTO SERVIRA' PER IL TRAINING E MI SERVIRANNO LE PIU INFLUENTI (LABELS NEEDED)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h60z43G_AUZP",
        "colab_type": "code",
        "outputId": "3eccc69a-99ec-4d36-9cbd-e018826a9773",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "#Counter vuole una lista di stringhe, quindi:\n",
        "list_all_tokenized_lemmed_wordscloud=all_tokenized_lemmed_wordscloud.split()\n",
        "counter_also_lemmed=Counter(list_all_tokenized_lemmed_wordscloud)\n",
        "\n",
        "#numero di tutte le parole lemmiz e stemmizz, no doppioni,\n",
        "#tra tutte le reviews labellate\n",
        "print(len(counter_also_lemmed)) \n",
        "\n",
        "\n",
        "most_common_also_lemmed_words = [common[0] for common in counter_also_lemmed.most_common()][:5000]\n",
        "#è una lista di stringhe, le 5000 parole lemm stem che compaiono piu volte in tutti i documenti labellati"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "25973\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4rXA8pLpB6NW",
        "colab_type": "text"
      },
      "source": [
        "CORPUS DEI LEMM STEMM: TRA LE PIU FREQUENTI TENGO SOLO LE INFLUENTI"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A8RSTQ5PCAza",
        "colab_type": "code",
        "outputId": "24ab505d-3f5d-4477-b118-0c7441092f88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "words = [word for word in most_common_also_lemmed_words if \n",
        "        (np.array(total_df['class'][:number_of_labelled]  == 'pos') & np.array(total_df['text_tokenized_lemmed'][:number_of_labelled].apply(lambda x: word in x))).sum() / np.array(total_df['text_tokenized_lemmed'][:number_of_labelled].apply(lambda x: word in x)).sum() > 0.75\n",
        "        or (np.array(total_df['class'][:number_of_labelled]  == 'pos') & np.array(total_df['text_tokenized_lemmed'][:number_of_labelled].apply(lambda x: word in x))).sum() / np.array(total_df['text_tokenized_lemmed'][:number_of_labelled].apply(lambda x: word in x)).sum() < 0.25]\n",
        "print(len(words))\n",
        "#è una lista di parole, senza doppioni, dalle labelled reviews, tokenizzate e lemmizate,\n",
        "#tra le 5k piu frequenti e piu influenti (per il 70% di quella prob...)\n",
        "#DEVO USARE QUESTE PER COSTRUIRE LA FEATURE FINALE \n",
        "#PER OGNI DOCUMENTO DEVO CONSIDERARE SOLO QUESTE"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1908\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fiKAcgRBDRN-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "words.append('non')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MDbFHw1sCjfR",
        "colab_type": "text"
      },
      "source": [
        "STATISTICHE SUI LEMMA RIMASTI NEL CORPUS words"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vV8flUNHCidt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "bfcd0150-195c-4536-8597-3ccac37ce0fa"
      },
      "source": [
        "#vedo quanto sono positive\n",
        "for word in words:\n",
        "    print(word, (np.array(total_df['class'][:number_of_labelled]  == 'pos') & np.array(total_df['text_tokenized_lemmed'][:number_of_labelled] .apply(lambda x: word in x))).sum() / np.array(total_df['text_tokenized_lemmed'][:number_of_labelled] .apply(lambda x: word in x)).sum(), \"nelle positive\")"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "personal 0.7667216903268406 nelle positive\n",
            "buon 0.7536263027828516 nelle positive\n",
            "ottim 0.8931841302136317 nelle positive\n",
            "pul 0.8024009603841536 nelle positive\n",
            "bell 0.7748097782094868 nelle positive\n",
            "ben 0.765008025682183 nelle positive\n",
            "grand 0.7661069225496916 nelle positive\n",
            "consigl 0.8089970501474927 nelle positive\n",
            "dispon 0.8381008552049543 nelle positive\n",
            "ristor 0.7804002052334531 nelle positive\n",
            "torn 0.7555357142857143 nelle positive\n",
            "min 0.761659100288898 nelle positive\n",
            "venez 0.7636880165289256 nelle positive\n",
            "gentil 0.7955302881787885 nelle positive\n",
            "pied 0.8203309692671394 nelle positive\n",
            "centr 0.8215521502718734 nelle positive\n",
            "stazion 0.7891321978913219 nelle positive\n",
            "sempr 0.7681751824817519 nelle positive\n",
            "ogni 0.7513559805880674 nelle positive\n",
            "cordial 0.8555667001003009 nelle positive\n",
            "comod 0.8092882660071449 nelle positive\n",
            "mil 0.7860544217687074 nelle positive\n",
            "citt 0.8249916022841787 nelle positive\n",
            "perfett 0.9331523583305056 nelle positive\n",
            "accogl 0.8374035989717223 nelle positive\n",
            "eccell 0.9381619405534677 nelle positive\n",
            "visit 0.8349335302806499 nelle positive\n",
            "confortevol 0.9190751445086706 nelle positive\n",
            "ferm 0.7973343786750294 nelle positive\n",
            "spazios 0.9114470842332614 nelle positive\n",
            "raggiung 0.8034351145038168 nelle positive\n",
            "scelt 0.7819253438113949 nelle positive\n",
            "cortes 0.7830336200156373 nelle positive\n",
            "tranquill 0.8866749688667497 nelle positive\n",
            "bellissim 0.8642611683848798 nelle positive\n",
            "cur 0.7966768692610406 nelle positive\n",
            "piac 0.8374823860967591 nelle positive\n",
            "canal 0.7952573742047426 nelle positive\n",
            "metr 0.7587740978744439 nelle positive\n",
            "piazz 0.8466135458167331 nelle positive\n",
            "central 0.7915479582146249 nelle positive\n",
            "professional 0.8130081300813008 nelle positive\n",
            "meravigl 0.8989473684210526 nelle positive\n",
            "ritorn 0.787288569964121 nelle positive\n",
            "viv 0.8150789607743251 nelle positive\n",
            "facil 0.8751357220412594 nelle positive\n",
            "gratu 0.8159065628476084 nelle positive\n",
            "ampi 0.8681818181818182 nelle positive\n",
            "autobus 0.8239083750894775 nelle positive\n",
            "abbond 0.895169578622816 nelle positive\n",
            "graz 0.8819951338199513 nelle positive\n",
            "piacevol 0.8669001751313485 nelle positive\n",
            "situ 0.8363740022805017 nelle positive\n",
            "sporc 0.0461750516884907 nelle positive\n",
            "spa 0.7645003494060097 nelle positive\n",
            "modern 0.8960190136660725 nelle positive\n",
            "famigl 0.768170426065163 nelle positive\n",
            "var 0.7915407854984894 nelle positive\n",
            "rapport 0.8337155963302753 nelle positive\n",
            "napol 0.7989130434782609 nelle positive\n",
            "tax 0.8023088023088023 nelle positive\n",
            "trascorr 0.8243405275779376 nelle positive\n",
            "terrazz 0.8218812541694464 nelle positive\n",
            "principal 0.773851590106007 nelle positive\n",
            "splendid 0.9112181693048864 nelle positive\n",
            "dolc 0.7986440677966101 nelle positive\n",
            "fantast 0.913658881811748 nelle positive\n",
            "staff 0.8545326774420239 nelle positive\n",
            "mal 0.23502994011976047 nelle positive\n",
            "sacc 0.765990639625585 nelle positive\n",
            "tren 0.7895595432300163 nelle positive\n",
            "fresc 0.8056618209640398 nelle positive\n",
            "distanz 0.831016825164594 nelle positive\n",
            "stil 0.7536231884057971 nelle positive\n",
            "fantastic 0.9219474497681608 nelle positive\n",
            "incantevol 0.8930817610062893 nelle positive\n",
            "particol 0.8095238095238095 nelle positive\n",
            "suit 0.7752081406105458 nelle positive\n",
            "turist 0.7565359477124183 nelle positive\n",
            "aeroport 0.844061650045331 nelle positive\n",
            "estrem 0.7620578778135049 nelle positive\n",
            "luog 0.7586484312148029 nelle positive\n",
            "eleg 0.9163144547759933 nelle positive\n",
            "rilass 0.8785529715762274 nelle positive\n",
            "brev 0.8361486486486487 nelle positive\n",
            "eccezional 0.8681506849315068 nelle positive\n",
            "rispond 0.21792260692464357 nelle positive\n",
            "apprezz 0.894874022589053 nelle positive\n",
            "scars 0.14462081128747795 nelle positive\n",
            "affacc 0.7752414398595259 nelle positive\n",
            "giardin 0.8184383819379115 nelle positive\n",
            "accoglit 0.7666948436179205 nelle positive\n",
            "ambient 0.7768014059753954 nelle positive\n",
            "metropolit 0.8616924476797089 nelle positive\n",
            "gust 0.8561215370866846 nelle positive\n",
            "special 0.7861811391223156 nelle positive\n",
            "incred 0.8712574850299402 nelle positive\n",
            "balcon 0.7797443461160275 nelle positive\n",
            "attent 0.8487848784878488 nelle positive\n",
            "panoram 0.8007774538386784 nelle positive\n",
            "deliz 0.9415841584158415 nelle positive\n",
            "catt 0.05952380952380952 nelle positive\n",
            "pont 0.838569880823402 nelle positive\n",
            "gentilissim 0.914018691588785 nelle positive\n",
            "bel 0.7717287488061128 nelle positive\n",
            "duom 0.9118217054263565 nelle positive\n",
            "silenz 0.8964552238805971 nelle positive\n",
            "super 0.8324439701173959 nelle positive\n",
            "soddisf 0.7931747673216132 nelle positive\n",
            "gentilezz 0.8143153526970954 nelle positive\n",
            "dot 0.8711111111111111 nelle positive\n",
            "relax 0.8393665158371041 nelle positive\n",
            "negoz 0.8293241695303551 nelle positive\n",
            "ferroviar 0.8080808080808081 nelle positive\n",
            "god 0.8354430379746836 nelle positive\n",
            "stup 0.8386336866902238 nelle positive\n",
            "prossim 0.7935779816513762 nelle positive\n",
            "navett 0.7519788918205804 nelle positive\n",
            "ideal 0.9136522753792299 nelle positive\n",
            "disponibil 0.778698224852071 nelle positive\n",
            "inclus 0.7536407766990292 nelle positive\n",
            "passegg 0.8477466504263094 nelle positive\n",
            "telefon 0.24893917963224893 nelle positive\n",
            "atmosfer 0.867080745341615 nelle positive\n",
            "nemmen 0.15853658536585366 nelle positive\n",
            "ricc 0.8930741190765492 nelle positive\n",
            "odor 0.1546811397557666 nelle positive\n",
            "scortes 0.07114624505928854 nelle positive\n",
            "vin 0.7862165963431786 nelle positive\n",
            "beness 0.7836338418862691 nelle positive\n",
            "dettagl 0.803088803088803 nelle positive\n",
            "simpat 0.8068331143232589 nelle positive\n",
            "vaporett 0.9031296572280179 nelle positive\n",
            "lin 0.7538461538461538 nelle positive\n",
            "pen 0.7544581618655692 nelle positive\n",
            "lag 0.7590822179732314 nelle positive\n",
            "storic 0.8887262079062958 nelle positive\n",
            "rialt 0.8820678513731826 nelle positive\n",
            "pulitissim 0.9546703296703297 nelle positive\n",
            "benissim 0.9057665260196905 nelle positive\n",
            "neanc 0.16149068322981366 nelle positive\n",
            "evit 0.24300441826215022 nelle positive\n",
            "palazz 0.8035426731078905 nelle positive\n",
            "strateg 0.8380281690140845 nelle positive\n",
            "pessim 0.03755868544600939 nelle positive\n",
            "veloc 0.7872023809523809 nelle positive\n",
            "impecc 0.948377581120944 nelle positive\n",
            "favol 0.8880248833592534 nelle positive\n",
            "efficient 0.8699690402476781 nelle positive\n",
            "esigt 0.852760736196319 nelle positive\n",
            "torin 0.851985559566787 nelle positive\n",
            "occasion 0.7647058823529411 nelle positive\n",
            "spettacol 0.8375209380234506 nelle positive\n",
            "cuor 0.8936170212765957 nelle positive\n",
            "attrazion 0.9275123558484349 nelle positive\n",
            "formagg 0.7565359477124183 nelle positive\n",
            "tipic 0.8079470198675497 nelle positive\n",
            "sugger 0.7589134125636672 nelle positive\n",
            "disporr 0.7538461538461538 nelle positive\n",
            "moquett 0.23898305084745763 nelle positive\n",
            "variet 0.8230383973288815 nelle positive\n",
            "disposizion 0.7783595113438045 nelle positive\n",
            "trasport 0.7967914438502673 nelle positive\n",
            "tè 0.7528301886792453 nelle positive\n",
            "bigliett 0.819838056680162 nelle positive\n",
            "ragionevol 0.8657243816254417 nelle positive\n",
            "scomod 0.23893805309734514 nelle positive\n",
            "comfort 0.8519793459552496 nelle positive\n",
            "divert 0.8401486988847584 nelle positive\n",
            "macc 0.07569721115537849 nelle positive\n",
            "minuscol 0.20522388059701493 nelle positive\n",
            "terribil 0.08203125 nelle positive\n",
            "barc 0.8640350877192983 nelle positive\n",
            "scad 0.09487666034155598 nelle positive\n",
            "volentier 0.9339285714285714 nelle positive\n",
            "ubic 0.7619047619047619 nelle positive\n",
            "soddisfatt 0.8726591760299626 nelle positive\n",
            "scus 0.13894736842105262 nelle positive\n",
            "compl 0.9124513618677043 nelle positive\n",
            "tett 0.797979797979798 nelle positive\n",
            "rom 0.7794432548179872 nelle positive\n",
            "mozzaf 0.8924949290060852 nelle positive\n",
            "luss 0.7564655172413793 nelle positive\n",
            "bus 0.8047722342733189 nelle positive\n",
            "selezion 0.7987152034261242 nelle positive\n",
            "confort 0.8448979591836735 nelle positive\n",
            "weekend 0.7653276955602537 nelle positive\n",
            "felic 0.8094218415417559 nelle positive\n",
            "comodissim 0.9178947368421052 nelle positive\n",
            "funzional 0.8565400843881856 nelle positive\n",
            "shopping 0.9311827956989247 nelle positive\n",
            "continental 0.8301886792452831 nelle positive\n",
            "ospital 0.7765726681127982 nelle positive\n",
            "vicinissim 0.8191489361702128 nelle positive\n",
            "verd 0.827354260089686 nelle positive\n",
            "romant 0.8662131519274376 nelle positive\n",
            "ringraz 0.8752834467120182 nelle positive\n",
            "muff 0.06038647342995169 nelle positive\n",
            "decor 0.7745535714285714 nelle positive\n",
            "parc 0.8317073170731707 nelle positive\n",
            "delusion 0.21149425287356322 nelle positive\n",
            "cereal 0.7910112359550562 nelle positive\n",
            "polv 0.08977556109725686 nelle positive\n",
            "puzz 0.0625 nelle positive\n",
            "sold 0.2324455205811138 nelle positive\n",
            "drink 0.7696078431372549 nelle positive\n",
            "attrezz 0.8624708624708625 nelle positive\n",
            "rott 0.09768637532133675 nelle positive\n",
            "caratterist 0.7932692307692307 nelle positive\n",
            "sconsigl 0.04567307692307692 nelle positive\n",
            "chef 0.8463611859838275 nelle positive\n",
            "affoll 0.7581047381546134 nelle positive\n",
            "util 0.7888040712468194 nelle positive\n",
            "consigliatissim 1.0 nelle positive\n",
            "gradevol 0.766497461928934 nelle positive\n",
            "inesistent 0.05305039787798409 nelle positive\n",
            "booking 0.21864951768488747 nelle positive\n",
            "class 0.8602150537634409 nelle positive\n",
            "crocier 0.7972508591065293 nelle positive\n",
            "affascin 0.8981233243967829 nelle positive\n",
            "calor 0.8210526315789474 nelle positive\n",
            "rovin 0.21448467966573817 nelle positive\n",
            "escursion 0.8542857142857143 nelle positive\n",
            "aperit 0.7565982404692082 nelle positive\n",
            "vill 0.7796052631578947 nelle positive\n",
            "convenient 0.7692307692307693 nelle positive\n",
            "neo 0.9510869565217391 nelle positive\n",
            "coccol 0.943342776203966 nelle positive\n",
            "junior 0.7944785276073619 nelle positive\n",
            "famil 0.8441926345609065 nelle positive\n",
            "sant 0.7876923076923077 nelle positive\n",
            "design 0.8221574344023324 nelle positive\n",
            "senz 0.9057142857142857 nelle positive\n",
            "romp 0.14540059347181009 nelle positive\n",
            "orribil 0.09316770186335403 nelle positive\n",
            "cad 0.23353293413173654 nelle positive\n",
            "estat 0.7567567567567568 nelle positive\n",
            "palestr 0.8157099697885196 nelle positive\n",
            "capell 0.15064102564102563 nelle positive\n",
            "esplor 0.8795180722891566 nelle positive\n",
            "concierg 0.7619047619047619 nelle positive\n",
            "gondol 0.9166666666666666 nelle positive\n",
            "bellezz 0.7537993920972644 nelle positive\n",
            "capr 0.8339483394833949 nelle positive\n",
            "maleduc 0.06430868167202572 nelle positive\n",
            "top 0.7889908256880734 nelle positive\n",
            "brav 0.7868852459016393 nelle positive\n",
            "dispiac 0.2389937106918239 nelle positive\n",
            "raffin 0.9096573208722741 nelle positive\n",
            "rapid 0.7586206896551724 nelle positive\n",
            "straordinar 0.9320388349514563 nelle positive\n",
            "vuot 0.21782178217821782 nelle positive\n",
            "squis 0.9365079365079365 nelle positive\n",
            "castell 0.8268551236749117 nelle positive\n",
            "affett 0.7679738562091504 nelle positive\n",
            "pizz 0.7556390977443609 nelle positive\n",
            "costier 0.9074074074074074 nelle positive\n",
            "scaric 0.23809523809523808 nelle positive\n",
            "piccolissim 0.07017543859649122 nelle positive\n",
            "traghett 0.8674698795180723 nelle positive\n",
            "grazios 0.8732876712328768 nelle positive\n",
            "sig 0.810126582278481 nelle positive\n",
            "montagn 0.7953667953667953 nelle positive\n",
            "palac 0.8023715415019763 nelle positive\n",
            "alt 0.9143835616438356 nelle positive\n",
            "buonissim 0.9295774647887324 nelle positive\n",
            "umid 0.18773946360153257 nelle positive\n",
            "magnif 0.9003436426116839 nelle positive\n",
            "fascin 0.8647686832740213 nelle positive\n",
            "recent 0.7777777777777778 nelle positive\n",
            "gustos 0.9244604316546763 nelle positive\n",
            "europe 0.7992700729927007 nelle positive\n",
            "esist 0.24436090225563908 nelle positive\n",
            "premett 0.23809523809523808 nelle positive\n",
            "lussuos 0.862453531598513 nelle positive\n",
            "indimentic 0.924812030075188 nelle positive\n",
            "vis 0.7509578544061303 nelle positive\n",
            "mapp 0.8110236220472441 nelle positive\n",
            "rimbors 0.0684931506849315 nelle positive\n",
            "accad 0.2145922746781116 nelle positive\n",
            "sogn 0.8744769874476988 nelle positive\n",
            "plastic 0.17427385892116182 nelle positive\n",
            "muse 0.9434782608695652 nelle positive\n",
            "pover 0.21686746987951808 nelle positive\n",
            "costring 0.1652892561983471 nelle positive\n",
            "internazional 0.7609561752988048 nelle positive\n",
            "miel 0.7688888888888888 nelle positive\n",
            "dintorn 0.7658730158730159 nelle positive\n",
            "nonc 0.8582995951417004 nelle positive\n",
            "magic 0.8771186440677966 nelle positive\n",
            "mand 0.2311111111111111 nelle positive\n",
            "stor 0.7551020408163265 nelle positive\n",
            "pegg 0.06437768240343347 nelle positive\n",
            "vast 0.8775510204081632 nelle positive\n",
            "mediocr 0.12184873949579832 nelle positive\n",
            "premur 0.9008264462809917 nelle positive\n",
            "rifiut 0.0593607305936073 nelle positive\n",
            "centralissim 0.8565573770491803 nelle positive\n",
            "sorrent 0.8858695652173914 nelle positive\n",
            "collin 0.8423423423423423 nelle positive\n",
            "suggest 0.8008658008658008 nelle positive\n",
            "foll 0.7948717948717948 nelle positive\n",
            "error 0.18446601941747573 nelle positive\n",
            "trist 0.20089285714285715 nelle positive\n",
            "peggior 0.05627705627705628 nelle positive\n",
            "compet 0.8177966101694916 nelle positive\n",
            "fatiscent 0.06666666666666667 nelle positive\n",
            "vergogn 0.06278026905829596 nelle positive\n",
            "water 0.14414414414414414 nelle positive\n",
            "tradizional 0.8790697674418605 nelle positive\n",
            "cocktail 0.7863636363636364 nelle positive\n",
            "gratuit 0.8198198198198198 nelle positive\n",
            "lav 0.16216216216216217 nelle positive\n",
            "minib 0.7511111111111111 nelle positive\n",
            "anniversar 0.8504672897196262 nelle positive\n",
            "paradis 0.9077669902912622 nelle positive\n",
            "bai 0.9226804123711341 nelle positive\n",
            "attegg 0.20772946859903382 nelle positive\n",
            "vall 0.8793969849246231 nelle positive\n",
            "buc 0.1111111111111111 nelle positive\n",
            "profum 0.7655502392344498 nelle positive\n",
            "golf 0.8 nelle positive\n",
            "esit 0.9049773755656109 nelle positive\n",
            "sci 0.8609271523178808 nelle positive\n",
            "amalfitan 0.9306930693069307 nelle positive\n",
            "circond 0.8101851851851852 nelle positive\n",
            "disag 0.23316062176165803 nelle positive\n",
            "assurd 0.10628019323671498 nelle positive\n",
            "original 0.7980295566502463 nelle positive\n",
            "ravell 0.9155844155844156 nelle positive\n",
            "allag 0.16748768472906403 nelle positive\n",
            "festegg 0.8267326732673267 nelle positive\n",
            "lod 0.7676767676767676 nelle positive\n",
            "pac 0.8131313131313131 nelle positive\n",
            "commercial 0.7653061224489796 nelle positive\n",
            "qualific 0.775 nelle positive\n",
            "lagun 0.8774193548387097 nelle positive\n",
            "comport 0.21808510638297873 nelle positive\n",
            "magnific 0.8969072164948454 nelle positive\n",
            "destr 0.7705882352941177 nelle positive\n",
            "boutiqu 0.9479166666666666 nelle positive\n",
            "cioccol 0.7877094972067039 nelle positive\n",
            "squallid 0.10160427807486631 nelle positive\n",
            "lumin 0.8402061855670103 nelle positive\n",
            "pizzer 0.786096256684492 nelle positive\n",
            "natur 0.8277777777777777 nelle positive\n",
            "bern 0.9395973154362416 nelle positive\n",
            "confezion 0.23243243243243245 nelle positive\n",
            "superl 0.9679144385026738 nelle positive\n",
            "pancett 0.786096256684492 nelle positive\n",
            "rar 0.8315217391304348 nelle positive\n",
            "loung 0.7931034482758621 nelle positive\n",
            "chies 0.8806818181818182 nelle positive\n",
            "incub 0.0893854748603352 nelle positive\n",
            "zer 0.11834319526627218 nelle positive\n",
            "spiegazion 0.21839080459770116 nelle positive\n",
            "galler 0.9080459770114943 nelle positive\n",
            "vesuv 0.9005847953216374 nelle positive\n",
            "immerg 0.9037433155080213 nelle positive\n",
            "amalf 0.9225806451612903 nelle positive\n",
            "sky 0.7541899441340782 nelle positive\n",
            "prosecc 0.8352941176470589 nelle positive\n",
            "marm 0.861271676300578 nelle positive\n",
            "tramont 0.8908045977011494 nelle positive\n",
            "piazzal 0.7975460122699386 nelle positive\n",
            "famos 0.7865168539325843 nelle positive\n",
            "agi 0.8516483516483516 nelle positive\n",
            "assort 0.8228571428571428 nelle positive\n",
            "pist 0.7987012987012987 nelle positive\n",
            "pubblicizz 0.2037037037037037 nelle positive\n",
            "grandissim 0.8522727272727273 nelle positive\n",
            "upgrad 0.7657142857142857 nelle positive\n",
            "tappet 0.24260355029585798 nelle positive\n",
            "imbarazz 0.23214285714285715 nelle positive\n",
            "insett 0.08053691275167785 nelle positive\n",
            "mont 0.757396449704142 nelle positive\n",
            "lungom 0.8538011695906432 nelle positive\n",
            "napolet 0.7515527950310559 nelle positive\n",
            "borg 0.8355263157894737 nelle positive\n",
            "teatr 0.8395061728395061 nelle positive\n",
            "salott 0.8809523809523809 nelle positive\n",
            "gioiell 0.9461077844311377 nelle positive\n",
            "cattiv 0.12574850299401197 nelle positive\n",
            "addebit 0.2080536912751678 nelle positive\n",
            "mol 0.8709677419354839 nelle positive\n",
            "piccolin 0.793939393939394 nelle positive\n",
            "angust 0.1746987951807229 nelle positive\n",
            "ridicol 0.14012738853503184 nelle positive\n",
            "varieg 0.8848484848484849 nelle positive\n",
            "cittadin 0.8050314465408805 nelle positive\n",
            "formic 0.058823529411764705 nelle positive\n",
            "articol 0.7672955974842768 nelle positive\n",
            "cifr 0.22784810126582278 nelle positive\n",
            "butt 0.13836477987421383 nelle positive\n",
            "tantissim 0.7672955974842768 nelle positive\n",
            "grott 0.8294573643410853 nelle positive\n",
            "assent 0.10897435897435898 nelle positive\n",
            "immers 0.9375 nelle positive\n",
            "accorg 0.2484076433121019 nelle positive\n",
            "memor 0.8417721518987342 nelle positive\n",
            "nav 0.7651515151515151 nelle positive\n",
            "ottimal 0.8129032258064516 nelle positive\n",
            "verific 0.2251655629139073 nelle positive\n",
            "plus 0.782051282051282 nelle positive\n",
            "invad 0.8407643312101911 nelle positive\n",
            "sanitar 0.21794871794871795 nelle positive\n",
            "salum 0.7727272727272727 nelle positive\n",
            "stad 0.9083969465648855 nelle positive\n",
            "fior 0.8639455782312925 nelle positive\n",
            "caos 0.8246753246753247 nelle positive\n",
            "inaccett 0.08 nelle positive\n",
            "arte 0.8299319727891157 nelle positive\n",
            "superb 0.9463087248322147 nelle positive\n",
            "rubinett 0.15436241610738255 nelle positive\n",
            "pasticcer 0.785234899328859 nelle positive\n",
            "ammir 0.8827586206896552 nelle positive\n",
            "scapp 0.2054794520547945 nelle positive\n",
            "intim 0.7931034482758621 nelle positive\n",
            "caffetter 0.7569444444444444 nelle positive\n",
            "ca 0.8141592920353983 nelle positive\n",
            "ostell 0.18110236220472442 nelle positive\n",
            "milanes 0.7642857142857142 nelle positive\n",
            "potenzial 0.11971830985915492 nelle positive\n",
            "ador 0.9103448275862069 nelle positive\n",
            "giustific 0.2108843537414966 nelle positive\n",
            "pel 0.17777777777777778 nelle positive\n",
            "lateral 0.8071428571428572 nelle positive\n",
            "aires 0.855072463768116 nelle positive\n",
            "schif 0.029850746268656716 nelle positive\n",
            "arrog 0.050359712230215826 nelle positive\n",
            "buenos 0.8540145985401459 nelle positive\n",
            "king 0.85 nelle positive\n",
            "siz 0.8561151079136691 nelle positive\n",
            "pedonal 0.9117647058823529 nelle positive\n",
            "neppur 0.11666666666666667 nelle positive\n",
            "spiac 0.16666666666666666 nelle positive\n",
            "moltissim 0.7794117647058824 nelle positive\n",
            "amichevol 0.8666666666666667 nelle positive\n",
            "caratt 0.8409090909090909 nelle positive\n",
            "annull 0.15315315315315314 nelle positive\n",
            "sgradevol 0.13432835820895522 nelle positive\n",
            "oas 0.9481481481481482 nelle positive\n",
            "disponibilissim 0.9416058394160584 nelle positive\n",
            "disastr 0.07633587786259542 nelle positive\n",
            "terminal 0.8214285714285714 nelle positive\n",
            "nuovissim 0.9044117647058824 nelle positive\n",
            "specific 0.2109375 nelle positive\n",
            "concert 0.8048780487804879 nelle positive\n",
            "improvvis 0.16923076923076924 nelle positive\n",
            "garibald 0.7916666666666666 nelle positive\n",
            "salon 0.782258064516129 nelle positive\n",
            "imbatt 0.8 nelle positive\n",
            "insist 0.2459016393442623 nelle positive\n",
            "sir 0.7777777777777778 nelle positive\n",
            "pol 0.7906976744186046 nelle positive\n",
            "alzar 0.2440944881889764 nelle positive\n",
            "tisan 0.7704918032786885 nelle positive\n",
            "sass 0.7575757575757576 nelle positive\n",
            "mens 0.08064516129032258 nelle positive\n",
            "disgust 0.03305785123966942 nelle positive\n",
            "bear 0.8372093023255814 nelle positive\n",
            "padron 0.7580645161290323 nelle positive\n",
            "cm 0.21052631578947367 nelle positive\n",
            "paesagg 0.8442622950819673 nelle positive\n",
            "bravissim 0.8583333333333333 nelle positive\n",
            "sfortun 0.13445378151260504 nelle positive\n",
            "miser 0.11570247933884298 nelle positive\n",
            "park 0.7904761904761904 nelle positive\n",
            "fogn 0.017543859649122806 nelle positive\n",
            "tub 0.19672131147540983 nelle positive\n",
            "epoc 0.7542372881355932 nelle positive\n",
            "malapen 0.13008130081300814 nelle positive\n",
            "navigl 0.8532110091743119 nelle positive\n",
            "estiv 0.7983193277310925 nelle positive\n",
            "natal 0.8073394495412844 nelle positive\n",
            "allegr 0.8034188034188035 nelle positive\n",
            "quotid 0.8220338983050848 nelle positive\n",
            "amor 0.8362068965517241 nelle positive\n",
            "genuin 0.9130434782608695 nelle positive\n",
            "sporciz 0.07964601769911504 nelle positive\n",
            "incant 0.8070175438596491 nelle positive\n",
            "coprilett 0.0782608695652174 nelle positive\n",
            "clim 0.7699115044247787 nelle positive\n",
            "pompe 0.883495145631068 nelle positive\n",
            "ignor 0.17857142857142858 nelle positive\n",
            "invid 0.7586206896551724 nelle positive\n",
            "team 0.8653846153846154 nelle positive\n",
            "nord 0.7699115044247787 nelle positive\n",
            "stradin 0.8584070796460177 nelle positive\n",
            "scherz 0.17857142857142858 nelle positive\n",
            "ingann 0.24761904761904763 nelle positive\n",
            "tradizion 0.918918918918919 nelle positive\n",
            "otrant 0.8118811881188119 nelle positive\n",
            "orient 0.7545454545454545 nelle positive\n",
            "insufficient 0.14814814814814814 nelle positive\n",
            "motel 0.16494845360824742 nelle positive\n",
            "inadegu 0.13761467889908258 nelle positive\n",
            "tentat 0.24074074074074073 nelle positive\n",
            "fre 0.7685185185185185 nelle positive\n",
            "discut 0.1523809523809524 nelle positive\n",
            "glutin 0.759493670886076 nelle positive\n",
            "spuntin 0.9158878504672897 nelle positive\n",
            "fint 0.15841584158415842 nelle positive\n",
            "trambust 0.9629629629629629 nelle positive\n",
            "riunion 0.7777777777777778 nelle positive\n",
            "perfezion 0.9523809523809523 nelle positive\n",
            "navig 0.803921568627451 nelle positive\n",
            "avven 0.24761904761904763 nelle positive\n",
            "conduzion 0.8888888888888888 nelle positive\n",
            "masser 0.8142857142857143 nelle positive\n",
            "salsicc 0.7904761904761904 nelle positive\n",
            "balconcin 0.7641509433962265 nelle positive\n",
            "snack 0.819047619047619 nelle positive\n",
            "logor 0.1262135922330097 nelle positive\n",
            "sbatt 0.13725490196078433 nelle positive\n",
            "contribu 0.8285714285714286 nelle positive\n",
            "piazzett 0.8947368421052632 nelle positive\n",
            "orrend 0.07692307692307693 nelle positive\n",
            "morbid 0.78 nelle positive\n",
            "telecomand 0.16483516483516483 nelle positive\n",
            "degust 0.8787878787878788 nelle positive\n",
            "pal 0.900990099009901 nelle positive\n",
            "polver 0.07216494845360824 nelle positive\n",
            "alimentar 0.7653061224489796 nelle positive\n",
            "emozion 0.7722772277227723 nelle positive\n",
            "malpens 0.8444444444444444 nelle positive\n",
            "divin 0.9405940594059405 nelle positive\n",
            "gamm 0.8181818181818182 nelle positive\n",
            "re 0.8061224489795918 nelle positive\n",
            "scrost 0.08333333333333333 nelle positive\n",
            "champagn 0.7934782608695652 nelle positive\n",
            "speriment 0.84375 nelle positive\n",
            "usur 0.20408163265306123 nelle positive\n",
            "prezios 0.8415841584158416 nelle positive\n",
            "immens 0.77 nelle positive\n",
            "pittoresc 0.8673469387755102 nelle positive\n",
            "alquant 0.17204301075268819 nelle positive\n",
            "eccellt 0.8181818181818182 nelle positive\n",
            "puzzolent 0.0707070707070707 nelle positive\n",
            "sfogliatell 0.8383838383838383 nelle positive\n",
            "bacon 0.8061224489795918 nelle positive\n",
            "line 0.81 nelle positive\n",
            "fitness 0.826530612244898 nelle positive\n",
            "rifug 0.8152173913043478 nelle positive\n",
            "curatissim 0.9797979797979798 nelle positive\n",
            "st 0.8043478260869565 nelle positive\n",
            "indecent 0.03225806451612903 nelle positive\n",
            "don 0.8085106382978723 nelle positive\n",
            "verand 0.8522727272727273 nelle positive\n",
            "torr 0.8241758241758241 nelle positive\n",
            "pos 0.18085106382978725 nelle positive\n",
            "gabinett 0.23076923076923078 nelle positive\n",
            "simpaticissim 0.8969072164948454 nelle positive\n",
            "maleodor 0.031578947368421054 nelle positive\n",
            "decad 0.17204301075268819 nelle positive\n",
            "imbarc 0.8260869565217391 nelle positive\n",
            "piacevolissim 0.968421052631579 nelle positive\n",
            "campagn 0.7894736842105263 nelle positive\n",
            "scarsissim 0.05319148936170213 nelle positive\n",
            "ognun 0.8021978021978022 nelle positive\n",
            "fabbric 0.775 nelle positive\n",
            "innamor 0.925531914893617 nelle positive\n",
            "spent 0.20224719101123595 nelle positive\n",
            "affabil 0.8043478260869565 nelle positive\n",
            "riand 0.8555555555555555 nelle positive\n",
            "mediterrane 0.9333333333333333 nelle positive\n",
            "sentier 0.9111111111111111 nelle positive\n",
            "ventol 0.21951219512195122 nelle positive\n",
            "seren 0.8461538461538461 nelle positive\n",
            "hyatt 0.8666666666666667 nelle positive\n",
            "nev 0.8941176470588236 nelle positive\n",
            "poliz 0.23076923076923078 nelle positive\n",
            "approssim 0.17204301075268819 nelle positive\n",
            "accur 0.8021978021978022 nelle positive\n",
            "tovagl 0.1744186046511628 nelle positive\n",
            "strapp 0.16666666666666666 nelle positive\n",
            "valentin 0.8133333333333334 nelle positive\n",
            "passion 0.8571428571428571 nelle positive\n",
            "ra 0.7619047619047619 nelle positive\n",
            "accadem 0.9090909090909091 nelle positive\n",
            "tiepid 0.2413793103448276 nelle positive\n",
            "tosc 0.8481012658227848 nelle positive\n",
            "strepit 0.8764044943820225 nelle positive\n",
            "chicc 0.7555555555555555 nelle positive\n",
            "bruc 0.21686746987951808 nelle positive\n",
            "freg 0.14285714285714285 nelle positive\n",
            "squar 0.8390804597701149 nelle positive\n",
            "charm 0.7764705882352941 nelle positive\n",
            "wow 0.8481012658227848 nelle positive\n",
            "moll 0.1 nelle positive\n",
            "toled 0.922077922077922 nelle positive\n",
            "obsolet 0.10588235294117647 nelle positive\n",
            "carl 0.8918918918918919 nelle positive\n",
            "denunc 0.23684210526315788 nelle positive\n",
            "caf 0.7560975609756098 nelle positive\n",
            "call 0.7605633802816901 nelle positive\n",
            "surgel 0.08974358974358974 nelle positive\n",
            "personalizz 0.8658536585365854 nelle positive\n",
            "vittor 0.8674698795180723 nelle positive\n",
            "polit 0.17333333333333334 nelle positive\n",
            "infiss 0.23170731707317074 nelle positive\n",
            "prestig 0.7857142857142857 nelle positive\n",
            "plaus 0.8809523809523809 nelle positive\n",
            "trascors 0.7682926829268293 nelle positive\n",
            "excelsior 0.8032786885245902 nelle positive\n",
            "protest 0.12658227848101267 nelle positive\n",
            "biciclett 0.9054054054054054 nelle positive\n",
            "abbass 0.24 nelle positive\n",
            "inutilizz 0.1 nelle positive\n",
            "pasticcin 0.8395061728395061 nelle positive\n",
            "ineccep 0.875 nelle positive\n",
            "acces 0.2222222222222222 nelle positive\n",
            "armon 0.875 nelle positive\n",
            "vivac 0.825 nelle positive\n",
            "elog 0.8076923076923077 nelle positive\n",
            "funicol 0.9113924050632911 nelle positive\n",
            "sgarb 0.06172839506172839 nelle positive\n",
            "crep 0.12658227848101267 nelle positive\n",
            "insopport 0.10256410256410256 nelle positive\n",
            "artist 0.8918918918918919 nelle positive\n",
            "spazzatur 0.18055555555555555 nelle positive\n",
            "ragnatel 0.05194805194805195 nelle positive\n",
            "arruggin 0.04054054054054054 nelle positive\n",
            "disorganizz 0.10256410256410256 nelle positive\n",
            "manigl 0.1267605633802817 nelle positive\n",
            "email 0.13114754098360656 nelle positive\n",
            "spiacevol 0.18421052631578946 nelle positive\n",
            "neg 0.14285714285714285 nelle positive\n",
            "maland 0.23076923076923078 nelle positive\n",
            "efficac 0.7866666666666666 nelle positive\n",
            "analcol 0.8243243243243243 nelle positive\n",
            "prai 0.9454545454545454 nelle positive\n",
            "barman 0.7532467532467533 nelle positive\n",
            "michelin 0.8484848484848485 nelle positive\n",
            "residenzial 0.881578947368421 nelle positive\n",
            "lampadin 0.1267605633802817 nelle positive\n",
            "restitu 0.22388059701492538 nelle positive\n",
            "bellin 0.8923076923076924 nelle positive\n",
            "marin 0.8208955223880597 nelle positive\n",
            "congel 0.1643835616438356 nelle positive\n",
            "profession 0.7887323943661971 nelle positive\n",
            "sobr 0.9305555555555556 nelle positive\n",
            "immang 0.0410958904109589 nelle positive\n",
            "chic 0.8666666666666667 nelle positive\n",
            "meeting 0.8235294117647058 nelle positive\n",
            "scomodissim 0.027777777777777776 nelle positive\n",
            "must 0.9041095890410958 nelle positive\n",
            "tendin 0.11267605633802817 nelle positive\n",
            "efficit 0.8356164383561644 nelle positive\n",
            "reclam 0.12857142857142856 nelle positive\n",
            "vinc 0.7571428571428571 nelle positive\n",
            "happy 0.8873239436619719 nelle positive\n",
            "malissim 0.04054054054054054 nelle positive\n",
            "mort 0.15492957746478872 nelle positive\n",
            "scandal 0.027777777777777776 nelle positive\n",
            "alilagun 0.9682539682539683 nelle positive\n",
            "cattedral 0.9117647058823529 nelle positive\n",
            "spacc 0.11267605633802817 nelle positive\n",
            "prat 0.7575757575757576 nelle positive\n",
            "hour 0.9 nelle positive\n",
            "candel 0.7857142857142857 nelle positive\n",
            "vernic 0.2112676056338028 nelle positive\n",
            "aspir 0.1267605633802817 nelle positive\n",
            "saponett 0.24285714285714285 nelle positive\n",
            "omelett 0.8714285714285714 nelle positive\n",
            "rifin 0.8428571428571429 nelle positive\n",
            "degrad 0.23943661971830985 nelle positive\n",
            "soffic 0.8714285714285714 nelle positive\n",
            "dic 0.12857142857142856 nelle positive\n",
            "contemporane 0.8840579710144928 nelle positive\n",
            "vetust 0.07142857142857142 nelle positive\n",
            "street 0.7971014492753623 nelle positive\n",
            "fenic 0.8666666666666667 nelle positive\n",
            "onda 0.8387096774193549 nelle positive\n",
            "chiss 0.13043478260869565 nelle positive\n",
            "sontuos 0.8714285714285714 nelle positive\n",
            "solarium 0.7611940298507462 nelle positive\n",
            "pietos 0.015625 nelle positive\n",
            "incrost 0.04477611940298507 nelle positive\n",
            "rumorosissim 0.03076923076923077 nelle positive\n",
            "celebr 0.8769230769230769 nelle positive\n",
            "plebisc 0.9253731343283582 nelle positive\n",
            "fiat 0.84375 nelle positive\n",
            "scoragg 0.7692307692307693 nelle positive\n",
            "deprim 0.07692307692307693 nelle positive\n",
            "avanz 0.24615384615384617 nelle positive\n",
            "trasand 0.19047619047619047 nelle positive\n",
            "arman 0.851063829787234 nelle positive\n",
            "freschissim 0.9402985074626866 nelle positive\n",
            "salottin 0.90625 nelle positive\n",
            "cestin 0.21666666666666667 nelle positive\n",
            "sorsegg 0.84375 nelle positive\n",
            "sublim 0.9692307692307692 nelle positive\n",
            "bonus 0.9090909090909091 nelle positive\n",
            "bancon 0.20634920634920634 nelle positive\n",
            "brill 0.7692307692307693 nelle positive\n",
            "autorizz 0.12727272727272726 nelle positive\n",
            "trav 0.8153846153846154 nelle positive\n",
            "squadr 0.8412698412698413 nelle positive\n",
            "stant 0.04838709677419355 nelle positive\n",
            "immacol 0.9538461538461539 nelle positive\n",
            "mattonell 0.12903225806451613 nelle positive\n",
            "godet 0.7846153846153846 nelle positive\n",
            "esaud 0.9230769230769231 nelle positive\n",
            "claustrofob 0.2 nelle positive\n",
            "tappezzer 0.24193548387096775 nelle positive\n",
            "sorprendent 0.828125 nelle positive\n",
            "rimostr 0.06349206349206349 nelle positive\n",
            "volum 0.23333333333333334 nelle positive\n",
            "abbin 0.7580645161290323 nelle positive\n",
            "arco 0.7931034482758621 nelle positive\n",
            "affresc 0.9166666666666666 nelle positive\n",
            "discussion 0.19298245614035087 nelle positive\n",
            "mah 0.05172413793103448 nelle positive\n",
            "compliment 0.8253968253968254 nelle positive\n",
            "termosifon 0.13559322033898305 nelle positive\n",
            "spum 0.7719298245614035 nelle positive\n",
            "maior 0.7636363636363637 nelle positive\n",
            "migl 0.8166666666666667 nelle positive\n",
            "castel 0.896551724137931 nelle positive\n",
            "scott 0.12903225806451613 nelle positive\n",
            "architettur 0.95 nelle positive\n",
            "four 0.7755102040816326 nelle positive\n",
            "insol 0.7833333333333333 nelle positive\n",
            "imbev 0.15 nelle positive\n",
            "truff 0.03773584905660377 nelle positive\n",
            "autent 0.8852459016393442 nelle positive\n",
            "milan 0.7843137254901961 nelle positive\n",
            "coragg 0.1206896551724138 nelle positive\n",
            "vecchissim 0.0 nelle positive\n",
            "gelid 0.10909090909090909 nelle positive\n",
            "residt 0.7884615384615384 nelle positive\n",
            "fals 0.10526315789473684 nelle positive\n",
            "indoman 0.10909090909090909 nelle positive\n",
            "cordialissim 0.95 nelle positive\n",
            "ston 0.8103448275862069 nelle positive\n",
            "grid 0.1724137931034483 nelle positive\n",
            "mitic 0.8909090909090909 nelle positive\n",
            "tapparell 0.21568627450980393 nelle positive\n",
            "ricopr 0.19298245614035087 nelle positive\n",
            "sporchissim 0.017543859649122806 nelle positive\n",
            "insapor 0.15254237288135594 nelle positive\n",
            "carus 0.9534883720930233 nelle positive\n",
            "arios 0.9298245614035088 nelle positive\n",
            "incompetent 0.01818181818181818 nelle positive\n",
            "funiv 0.8888888888888888 nelle positive\n",
            "bassissim 0.21428571428571427 nelle positive\n",
            "assapor 0.8421052631578947 nelle positive\n",
            "furt 0.1111111111111111 nelle positive\n",
            "sus 0.8035714285714286 nelle positive\n",
            "tecnolog 0.8181818181818182 nelle positive\n",
            "scontros 0.10909090909090909 nelle positive\n",
            "sforn 0.7857142857142857 nelle positive\n",
            "bid 0.05263157894736842 nelle positive\n",
            "schien 0.23636363636363636 nelle positive\n",
            "distingu 0.7678571428571429 nelle positive\n",
            "penos 0.05555555555555555 nelle positive\n",
            "cioccolatin 0.9454545454545454 nelle positive\n",
            "sconsigliatissim 0.017857142857142856 nelle positive\n",
            "disper 0.19642857142857142 nelle positive\n",
            "abbracc 0.9444444444444444 nelle positive\n",
            "incapac 0.03636363636363636 nelle positive\n",
            "scarn 0.14814814814814814 nelle positive\n",
            "soft 0.7777777777777778 nelle positive\n",
            "brer 0.9245283018867925 nelle positive\n",
            "evitatel 0.05555555555555555 nelle positive\n",
            "dozzinal 0.16363636363636364 nelle positive\n",
            "bur 0.9019607843137255 nelle positive\n",
            "lcd 0.8867924528301887 nelle positive\n",
            "oce 0.9791666666666666 nelle positive\n",
            "rubinetter 0.1111111111111111 nelle positive\n",
            "caracciol 0.86 nelle positive\n",
            "en 0.7884615384615384 nelle positive\n",
            "emergt 0.23529411764705882 nelle positive\n",
            "discrezion 0.9433962264150944 nelle positive\n",
            "microscop 0.05660377358490566 nelle positive\n",
            "gourmet 0.8 nelle positive\n",
            "scost 0.07547169811320754 nelle positive\n",
            "intonac 0.16666666666666666 nelle positive\n",
            "zoomarin 0.8043478260869565 nelle positive\n",
            "coordin 0.7735849056603774 nelle positive\n",
            "occhiell 0.9074074074074074 nelle positive\n",
            "scorbut 0.11764705882352941 nelle positive\n",
            "moral 0.07692307692307693 nelle positive\n",
            "metall 0.17307692307692307 nelle positive\n",
            "antiquar 0.8627450980392157 nelle positive\n",
            "disordin 0.21568627450980393 nelle positive\n",
            "impag 0.8846153846153846 nelle positive\n",
            "sempion 0.9787234042553191 nelle positive\n",
            "sciacquon 0.13725490196078433 nelle positive\n",
            "ovo 0.9038461538461539 nelle positive\n",
            "wireless 0.7884615384615384 nelle positive\n",
            "spruzz 0.22448979591836735 nelle positive\n",
            "york 0.7551020408163265 nelle positive\n",
            "pianofort 0.9069767441860465 nelle positive\n",
            "itinerar 0.9019607843137255 nelle positive\n",
            "alcol 0.8076923076923077 nelle positive\n",
            "cimic 0.027777777777777776 nelle positive\n",
            "eccentr 0.8823529411764706 nelle positive\n",
            "arrang 0.22 nelle positive\n",
            "ruggin 0.12244897959183673 nelle positive\n",
            "lov 0.9772727272727273 nelle positive\n",
            "scarafagg 0.0 nelle positive\n",
            "fium 0.7555555555555555 nelle positive\n",
            "alzat 0.16326530612244897 nelle positive\n",
            "frattemp 0.1 nelle positive\n",
            "darm 0.1836734693877551 nelle positive\n",
            "juventus 0.8604651162790697 nelle positive\n",
            "hop 0.9393939393939394 nelle positive\n",
            "antipat 0.1836734693877551 nelle positive\n",
            "trem 0.08333333333333333 nelle positive\n",
            "convint 0.21568627450980393 nelle positive\n",
            "equipagg 0.8 nelle positive\n",
            "sprovvist 0.19607843137254902 nelle positive\n",
            "impropon 0.12 nelle positive\n",
            "scoglier 0.8666666666666667 nelle positive\n",
            "san 0.7872340425531915 nelle positive\n",
            "informal 0.8163265306122449 nelle positive\n",
            "culinar 0.7551020408163265 nelle positive\n",
            "ros 0.7777777777777778 nelle positive\n",
            "allucin 0.06382978723404255 nelle positive\n",
            "arrabb 0.125 nelle positive\n",
            "equilibr 0.8979591836734694 nelle positive\n",
            "lettur 0.76 nelle positive\n",
            "umbri 0.875 nelle positive\n",
            "rigener 0.8775510204081632 nelle positive\n",
            "complement 0.7608695652173914 nelle positive\n",
            "bug 0.08695652173913043 nelle positive\n",
            "stuzzichin 0.7959183673469388 nelle positive\n",
            "insonn 0.08695652173913043 nelle positive\n",
            "dit 0.24489795918367346 nelle positive\n",
            "domin 0.7551020408163265 nelle positive\n",
            "vabb 0.075 nelle positive\n",
            "disegn 0.8913043478260869 nelle positive\n",
            "palm 0.7857142857142857 nelle positive\n",
            "sospir 0.7959183673469388 nelle positive\n",
            "cinem 0.7708333333333334 nelle positive\n",
            "brunc 0.8205128205128205 nelle positive\n",
            "autentic 0.9545454545454546 nelle positive\n",
            "bont 0.8333333333333334 nelle positive\n",
            "estend 0.8125 nelle positive\n",
            "oster 0.7777777777777778 nelle positive\n",
            "quotidian 0.8913043478260869 nelle positive\n",
            "ottur 0.0625 nelle positive\n",
            "sciocc 0.10869565217391304 nelle positive\n",
            "litig 0.1111111111111111 nelle positive\n",
            "ricchissim 0.8958333333333334 nelle positive\n",
            "elegant 0.9361702127659575 nelle positive\n",
            "convent 0.8409090909090909 nelle positive\n",
            "ingannevol 0.021739130434782608 nelle positive\n",
            "acquat 0.7659574468085106 nelle positive\n",
            "spogl 0.2127659574468085 nelle positive\n",
            "egiz 0.9130434782608695 nelle positive\n",
            "disinteress 0.08695652173913043 nelle positive\n",
            "gigl 0.9736842105263158 nelle positive\n",
            "ciclabil 0.9534883720930233 nelle positive\n",
            "cadorn 0.9090909090909091 nelle positive\n",
            "cannareg 0.8666666666666667 nelle positive\n",
            "ribad 0.2 nelle positive\n",
            "canad 0.9047619047619048 nelle positive\n",
            "sangu 0.1590909090909091 nelle positive\n",
            "tristezz 0.06521739130434782 nelle positive\n",
            "lentissim 0.13333333333333333 nelle positive\n",
            "art 0.8333333333333334 nelle positive\n",
            "jw 0.8 nelle positive\n",
            "territor 0.8863636363636364 nelle positive\n",
            "primaver 0.8043478260869565 nelle positive\n",
            "inquiet 0.13043478260869565 nelle positive\n",
            "competit 0.7674418604651163 nelle positive\n",
            "carit 0.17391304347826086 nelle positive\n",
            "cement 0.13636363636363635 nelle positive\n",
            "dt 0.85 nelle positive\n",
            "mark 0.813953488372093 nelle positive\n",
            "chiai 0.9 nelle positive\n",
            "ciprian 0.9130434782608695 nelle positive\n",
            "prefert 0.7555555555555555 nelle positive\n",
            "diffusion 0.8222222222222222 nelle positive\n",
            "brusc 0.13636363636363635 nelle positive\n",
            "scocc 0.023809523809523808 nelle positive\n",
            "nataliz 0.8863636363636364 nelle positive\n",
            "toller 0.19047619047619047 nelle positive\n",
            "intas 0.16279069767441862 nelle positive\n",
            "dolom 0.9534883720930233 nelle positive\n",
            "veron 0.9 nelle positive\n",
            "bic 0.8048780487804879 nelle positive\n",
            "golden 0.7777777777777778 nelle positive\n",
            "prepag 0.24390243902439024 nelle positive\n",
            "overbooking 0.1951219512195122 nelle positive\n",
            "addoss 0.1590909090909091 nelle positive\n",
            "repubbl 0.7619047619047619 nelle positive\n",
            "bor 0.7619047619047619 nelle positive\n",
            "orgogl 0.8333333333333334 nelle positive\n",
            "cosiddett 0.06976744186046512 nelle positive\n",
            "battell 0.8372093023255814 nelle positive\n",
            "infam 0.20454545454545456 nelle positive\n",
            "abbondantissim 1.0 nelle positive\n",
            "prelibatezz 0.8809523809523809 nelle positive\n",
            "tracc 0.24390243902439024 nelle positive\n",
            "cultur 0.8095238095238095 nelle positive\n",
            "lw 0.868421052631579 nelle positive\n",
            "engspa 0.868421052631579 nelle positive\n",
            "indifferent 0.18604651162790697 nelle positive\n",
            "trenin 0.8571428571428571 nelle positive\n",
            "svag 0.8372093023255814 nelle positive\n",
            "ammuff 0.046511627906976744 nelle positive\n",
            "umbert 0.9230769230769231 nelle positive\n",
            "arricc 0.8809523809523809 nelle positive\n",
            "grandios 0.8837209302325582 nelle positive\n",
            "decuman 0.972972972972973 nelle positive\n",
            "disserviz 0.075 nelle positive\n",
            "fettin 0.05 nelle positive\n",
            "mensol 0.23809523809523808 nelle positive\n",
            "inaspett 0.8095238095238095 nelle positive\n",
            "mauriz 0.9230769230769231 nelle positive\n",
            "seasons 0.78125 nelle positive\n",
            "sottovalut 0.9047619047619048 nelle positive\n",
            "solid 0.7804878048780488 nelle positive\n",
            "liquid 0.2 nelle positive\n",
            "bosc 0.8292682926829268 nelle positive\n",
            "svuot 0.12195121951219512 nelle positive\n",
            "fratell 0.8333333333333334 nelle positive\n",
            "villin 0.8 nelle positive\n",
            "derub 0.2 nelle positive\n",
            "dirott 0.14634146341463414 nelle positive\n",
            "segret 0.8048780487804879 nelle positive\n",
            "archeolog 0.875 nelle positive\n",
            "stent 0.075 nelle positive\n",
            "minim 0.19047619047619047 nelle positive\n",
            "discount 0.047619047619047616 nelle positive\n",
            "divid 0.15 nelle positive\n",
            "cormor 0.9696969696969697 nelle positive\n",
            "lezion 0.7692307692307693 nelle positive\n",
            "disd 0.15789473684210525 nelle positive\n",
            "trevis 0.8787878787878788 nelle positive\n",
            "carinissim 0.9 nelle positive\n",
            "passabil 0.0975609756097561 nelle positive\n",
            "minacc 0.23076923076923078 nelle positive\n",
            "alliet 0.7804878048780488 nelle positive\n",
            "enzo 0.9722222222222222 nelle positive\n",
            "biolog 0.8333333333333334 nelle positive\n",
            "piattin 0.1794871794871795 nelle positive\n",
            "aspirapolv 0.1282051282051282 nelle positive\n",
            "coron 0.8205128205128205 nelle positive\n",
            "quiet 0.825 nelle positive\n",
            "fass 0.96875 nelle positive\n",
            "sconvolg 0.1282051282051282 nelle positive\n",
            "insipid 0.12195121951219512 nelle positive\n",
            "orta 0.7647058823529411 nelle positive\n",
            "metropol 0.7777777777777778 nelle positive\n",
            "marks 0.8974358974358975 nelle positive\n",
            "bulgar 0.8387096774193549 nelle positive\n",
            "raffinatezz 0.925 nelle positive\n",
            "desol 0.1794871794871795 nelle positive\n",
            "decant 0.10526315789473684 nelle positive\n",
            "alpe 0.8709677419354839 nelle positive\n",
            "cultural 0.85 nelle positive\n",
            "dann 0.13157894736842105 nelle positive\n",
            "mors 0.15151515151515152 nelle positive\n",
            "azzecc 0.875 nelle positive\n",
            "modernissim 0.9 nelle positive\n",
            "ghett 0.8108108108108109 nelle positive\n",
            "fuoriusc 0.175 nelle positive\n",
            "etna 0.918918918918919 nelle positive\n",
            "maestr 0.868421052631579 nelle positive\n",
            "beac 0.7714285714285715 nelle positive\n",
            "capoline 0.868421052631579 nelle positive\n",
            "dirc 0.15384615384615385 nelle positive\n",
            "perenn 0.2222222222222222 nelle positive\n",
            "acque 0.8974358974358975 nelle positive\n",
            "ricuoc 0.7777777777777778 nelle positive\n",
            "rivisit 0.9230769230769231 nelle positive\n",
            "ligur 0.8108108108108109 nelle positive\n",
            "dor 0.7941176470588235 nelle positive\n",
            "fed 0.2222222222222222 nelle positive\n",
            "schegg 0.02857142857142857 nelle positive\n",
            "piccion 0.19444444444444445 nelle positive\n",
            "superficial 0.10526315789473684 nelle positive\n",
            "tovagliol 0.13513513513513514 nelle positive\n",
            "preventiv 0.13333333333333333 nelle positive\n",
            "cristallin 0.8918918918918919 nelle positive\n",
            "uccid 0.17647058823529413 nelle positive\n",
            "infest 0.08108108108108109 nelle positive\n",
            "uliv 0.9166666666666666 nelle positive\n",
            "illimit 0.868421052631579 nelle positive\n",
            "consegu 0.21052631578947367 nelle positive\n",
            "dimor 0.7837837837837838 nelle positive\n",
            "urlo 0.1111111111111111 nelle positive\n",
            "tennis 0.8 nelle positive\n",
            "rustic 0.8378378378378378 nelle positive\n",
            "attribu 0.10526315789473684 nelle positive\n",
            "ristorantin 0.7894736842105263 nelle positive\n",
            "medieval 0.7777777777777778 nelle positive\n",
            "automobil 0.7567567567567568 nelle positive\n",
            "esempl 0.9714285714285714 nelle positive\n",
            "serratur 0.17142857142857143 nelle positive\n",
            "bettol 0.0 nelle positive\n",
            "innov 0.9411764705882353 nelle positive\n",
            "episod 0.20588235294117646 nelle positive\n",
            "appiccic 0.1111111111111111 nelle positive\n",
            "schiamazz 0.24324324324324326 nelle positive\n",
            "camin 0.8108108108108109 nelle positive\n",
            "gelater 0.8571428571428571 nelle positive\n",
            "delic 0.8918918918918919 nelle positive\n",
            "pignol 0.7777777777777778 nelle positive\n",
            "malfunzion 0.1891891891891892 nelle positive\n",
            "catod 0.13513513513513514 nelle positive\n",
            "barzellett 0.13513513513513514 nelle positive\n",
            "finement 0.9459459459459459 nelle positive\n",
            "figuriamoc 0.11764705882352941 nelle positive\n",
            "spritz 0.8571428571428571 nelle positive\n",
            "ricicl 0.14705882352941177 nelle positive\n",
            "et 0.875 nelle positive\n",
            "appien 0.8857142857142857 nelle positive\n",
            "solegg 0.7777777777777778 nelle positive\n",
            "brezz 0.8333333333333334 nelle positive\n",
            "giustiz 0.8285714285714286 nelle positive\n",
            "of 0.7666666666666667 nelle positive\n",
            "cartolin 0.8055555555555556 nelle positive\n",
            "appassion 0.8285714285714286 nelle positive\n",
            "silenziosissim 0.9722222222222222 nelle positive\n",
            "urin 0.0 nelle positive\n",
            "bio 0.875 nelle positive\n",
            "sapient 0.9428571428571428 nelle positive\n",
            "wagner 0.7714285714285715 nelle positive\n",
            "carton 0.14705882352941177 nelle positive\n",
            "pasite 0.9166666666666666 nelle positive\n",
            "servizievol 0.8285714285714286 nelle positive\n",
            "annacqu 0.14285714285714285 nelle positive\n",
            "dannegg 0.23529411764705882 nelle positive\n",
            "nas 0.21212121212121213 nelle positive\n",
            "sblocc 0.18518518518518517 nelle positive\n",
            "intelligent 0.7714285714285715 nelle positive\n",
            "fragol 0.8571428571428571 nelle positive\n",
            "salentin 0.7714285714285715 nelle positive\n",
            "aost 0.875 nelle positive\n",
            "umbro 0.9411764705882353 nelle positive\n",
            "caramell 0.84375 nelle positive\n",
            "pancak 0.8529411764705882 nelle positive\n",
            "individual 0.7941176470588235 nelle positive\n",
            "argoment 0.12121212121212122 nelle positive\n",
            "portic 0.8484848484848485 nelle positive\n",
            "sforzesc 0.8823529411764706 nelle positive\n",
            "lev 0.22580645161290322 nelle positive\n",
            "ragn 0.03125 nelle positive\n",
            "movid 0.9705882352941176 nelle positive\n",
            "cromoterap 0.9411764705882353 nelle positive\n",
            "ladr 0.12903225806451613 nelle positive\n",
            "sudic 0.15151515151515152 nelle positive\n",
            "ripul 0.23529411764705882 nelle positive\n",
            "tratten 0.21875 nelle positive\n",
            "mare 0.8214285714285714 nelle positive\n",
            "martin 0.8620689655172413 nelle positive\n",
            "becher 0.8636363636363636 nelle positive\n",
            "artific 0.8484848484848485 nelle positive\n",
            "monument 0.9090909090909091 nelle positive\n",
            "ceram 0.7741935483870968 nelle positive\n",
            "gps 0.8275862068965517 nelle positive\n",
            "seggiov 0.8571428571428571 nelle positive\n",
            "equo 0.78125 nelle positive\n",
            "genov 0.8484848484848485 nelle positive\n",
            "soll 0.7878787878787878 nelle positive\n",
            "peopl 0.9032258064516129 nelle positive\n",
            "bologn 0.8387096774193549 nelle positive\n",
            "antonin 1.0 nelle positive\n",
            "perug 0.9310344827586207 nelle positive\n",
            "impolver 0.0 nelle positive\n",
            "sparecc 0.1 nelle positive\n",
            "frenet 0.90625 nelle positive\n",
            "utilissim 0.9090909090909091 nelle positive\n",
            "velocissim 0.9696969696969697 nelle positive\n",
            "penal 0.16 nelle positive\n",
            "decenz 0.03125 nelle positive\n",
            "savoi 0.9032258064516129 nelle positive\n",
            "garn 0.8333333333333334 nelle positive\n",
            "ante 0.1875 nelle positive\n",
            "dolcissim 0.9354838709677419 nelle positive\n",
            "tristissim 0.16666666666666666 nelle positive\n",
            "contraddistingu 0.8709677419354839 nelle positive\n",
            "stadium 0.8 nelle positive\n",
            "ragazzin 0.1935483870967742 nelle positive\n",
            "incustod 0.22580645161290322 nelle positive\n",
            "ginocc 0.2413793103448276 nelle positive\n",
            "led 0.78125 nelle positive\n",
            "templ 1.0 nelle positive\n",
            "infernal 0.16129032258064516 nelle positive\n",
            "nastr 0.13793103448275862 nelle positive\n",
            "rafferm 0.0 nelle positive\n",
            "artigianal 0.8064516129032258 nelle positive\n",
            "micr 0.0967741935483871 nelle positive\n",
            "fortissim 0.16129032258064516 nelle positive\n",
            "cortesissim 0.7741935483870968 nelle positive\n",
            "provoc 0.23333333333333334 nelle positive\n",
            "novit 0.8387096774193549 nelle positive\n",
            "splendor 0.8064516129032258 nelle positive\n",
            "autostradal 0.7741935483870968 nelle positive\n",
            "lingott 0.9285714285714286 nelle positive\n",
            "sommar 0.06666666666666667 nelle positive\n",
            "signoril 0.9 nelle positive\n",
            "ercol 0.9285714285714286 nelle positive\n",
            "orror 0.06666666666666667 nelle positive\n",
            "mover 0.9310344827586207 nelle positive\n",
            "spadar 1.0 nelle positive\n",
            "allen 0.8064516129032258 nelle positive\n",
            "sped 0.2413793103448276 nelle positive\n",
            "pales 0.03333333333333333 nelle positive\n",
            "pianist 0.9 nelle positive\n",
            "pap 0.7586206896551724 nelle positive\n",
            "perdet 0.9666666666666667 nelle positive\n",
            "sottilissim 0.12903225806451613 nelle positive\n",
            "colombin 0.8695652173913043 nelle positive\n",
            "piccin 0.8064516129032258 nelle positive\n",
            "freddissim 0.1 nelle positive\n",
            "sius 0.8571428571428571 nelle positive\n",
            "deodor 0.06666666666666667 nelle positive\n",
            "dirm 0.12903225806451613 nelle positive\n",
            "dorsodur 0.8620689655172413 nelle positive\n",
            "attracc 0.8 nelle positive\n",
            "vegan 0.8620689655172413 nelle positive\n",
            "lancaster 0.9230769230769231 nelle positive\n",
            "spaccanapol 1.0 nelle positive\n",
            "antincend 0.16 nelle positive\n",
            "brevissim 0.8 nelle positive\n",
            "roof 0.8666666666666667 nelle positive\n",
            "inespert 0.2 nelle positive\n",
            "presumibil 0.2222222222222222 nelle positive\n",
            "cappell 0.7666666666666667 nelle positive\n",
            "sofistic 0.8666666666666667 nelle positive\n",
            "approd 0.8620689655172413 nelle positive\n",
            "cafon 0.07692307692307693 nelle positive\n",
            "assord 0.06896551724137931 nelle positive\n",
            "loret 0.8275862068965517 nelle positive\n",
            "sconcert 0.13333333333333333 nelle positive\n",
            "assis 0.96 nelle positive\n",
            "sfizios 0.8666666666666667 nelle positive\n",
            "eserciz 0.8666666666666667 nelle positive\n",
            "calcol 0.17857142857142858 nelle positive\n",
            "miracol 0.13333333333333333 nelle positive\n",
            "station 0.8620689655172413 nelle positive\n",
            "leuc 0.7586206896551724 nelle positive\n",
            "vomer 0.9285714285714286 nelle positive\n",
            "vig 0.896551724137931 nelle positive\n",
            "locul 0.07407407407407407 nelle positive\n",
            "baldacchin 0.8333333333333334 nelle positive\n",
            "dog 0.8571428571428571 nelle positive\n",
            "tranquillissim 0.8620689655172413 nelle positive\n",
            "caparr 0.12 nelle positive\n",
            "prostit 0.1111111111111111 nelle positive\n",
            "dolcett 0.7931034482758621 nelle positive\n",
            "saturn 0.9047619047619048 nelle positive\n",
            "sensazional 0.9642857142857143 nelle positive\n",
            "cresp 0.9047619047619048 nelle positive\n",
            "incass 0.18518518518518517 nelle positive\n",
            "aggiust 0.10344827586206896 nelle positive\n",
            "ritorner 0.896551724137931 nelle positive\n",
            "eman 0.17857142857142858 nelle positive\n",
            "scenograf 0.8148148148148148 nelle positive\n",
            "pomez 0.9285714285714286 nelle positive\n",
            "monaster 0.8928571428571429 nelle positive\n",
            "convert 0.7857142857142857 nelle positive\n",
            "residu 0.06896551724137931 nelle positive\n",
            "spazz 0.14285714285714285 nelle positive\n",
            "mondial 0.76 nelle positive\n",
            "fenomenal 0.9655172413793104 nelle positive\n",
            "spaziosissim 0.9655172413793104 nelle positive\n",
            "sirol 0.9090909090909091 nelle positive\n",
            "incastr 0.14285714285714285 nelle positive\n",
            "ragg 0.8571428571428571 nelle positive\n",
            "decenn 0.1724137931034483 nelle positive\n",
            "stupef 0.896551724137931 nelle positive\n",
            "bruttissim 0.1111111111111111 nelle positive\n",
            "dock 0.84 nelle positive\n",
            "madonn 0.7857142857142857 nelle positive\n",
            "sfior 0.8148148148148148 nelle positive\n",
            "rifil 0.07407407407407407 nelle positive\n",
            "defil 0.896551724137931 nelle positive\n",
            "imprepar 0.07142857142857142 nelle positive\n",
            "friendly 0.8148148148148148 nelle positive\n",
            "sagred 0.8571428571428571 nelle positive\n",
            "dedizion 0.7777777777777778 nelle positive\n",
            "insult 0.03571428571428571 nelle positive\n",
            "manopol 0.14814814814814814 nelle positive\n",
            "infern 0.18518518518518517 nelle positive\n",
            "trascuratezz 0.17857142857142858 nelle positive\n",
            "sottotett 0.1111111111111111 nelle positive\n",
            "insistt 0.18518518518518517 nelle positive\n",
            "abbaz 0.9583333333333334 nelle positive\n",
            "inguard 0.0 nelle positive\n",
            "lurid 0.0 nelle positive\n",
            "inservient 0.10714285714285714 nelle positive\n",
            "fabriz 0.9642857142857143 nelle positive\n",
            "foscar 0.7777777777777778 nelle positive\n",
            "risarc 0.07407407407407407 nelle positive\n",
            "imperd 0.9285714285714286 nelle positive\n",
            "campanil 0.7857142857142857 nelle positive\n",
            "sush 0.875 nelle positive\n",
            "raffael 0.8333333333333334 nelle positive\n",
            "contratt 0.11538461538461539 nelle positive\n",
            "fs 0.9285714285714286 nelle positive\n",
            "viscont 0.92 nelle positive\n",
            "massim 0.9230769230769231 nelle positive\n",
            "tavolett 0.037037037037037035 nelle positive\n",
            "r 0.7916666666666666 nelle positive\n",
            "scettic 0.9285714285714286 nelle positive\n",
            "scav 0.8148148148148148 nelle positive\n",
            "actv 0.9615384615384616 nelle positive\n",
            "strettissim 0.1111111111111111 nelle positive\n",
            "cuman 0.9259259259259259 nelle positive\n",
            "sanp 0.9444444444444444 nelle positive\n",
            "ampli 0.8148148148148148 nelle positive\n",
            "commett 0.11538461538461539 nelle positive\n",
            "balen 1.0 nelle positive\n",
            "silv 0.7777777777777778 nelle positive\n",
            "ducal 0.7692307692307693 nelle positive\n",
            "fulmin 0.23076923076923078 nelle positive\n",
            "infel 0.24 nelle positive\n",
            "sudor 0.12 nelle positive\n",
            "durissim 0.15384615384615385 nelle positive\n",
            "stern 0.85 nelle positive\n",
            "scontr 0.17391304347826086 nelle positive\n",
            "squallor 0.08 nelle positive\n",
            "colm 0.16 nelle positive\n",
            "sopravvalut 0.14814814814814814 nelle positive\n",
            "pontil 0.8888888888888888 nelle positive\n",
            "coperc 0.23076923076923078 nelle positive\n",
            "light 0.7916666666666666 nelle positive\n",
            "liev 0.8148148148148148 nelle positive\n",
            "pin 0.8461538461538461 nelle positive\n",
            "efficent 0.9230769230769231 nelle positive\n",
            "rho 0.8888888888888888 nelle positive\n",
            "zaccar 1.0 nelle positive\n",
            "giorgion 0.782608695652174 nelle positive\n",
            "imparegg 0.8461538461538461 nelle positive\n",
            "barocc 0.7692307692307693 nelle positive\n",
            "naus 0.0 nelle positive\n",
            "suddett 0.24 nelle positive\n",
            "fuorv 0.18518518518518517 nelle positive\n",
            "centimetr 0.15384615384615385 nelle positive\n",
            "tony 0.9565217391304348 nelle positive\n",
            "insoddisfatt 0.2 nelle positive\n",
            "riviv 0.84 nelle positive\n",
            "freschezz 0.8333333333333334 nelle positive\n",
            "quantomen 0.07692307692307693 nelle positive\n",
            "correttezz 0.23076923076923078 nelle positive\n",
            "win 0.8095238095238095 nelle positive\n",
            "facilissim 0.9615384615384616 nelle positive\n",
            "socievol 0.7692307692307693 nelle positive\n",
            "forum 0.7916666666666666 nelle positive\n",
            "malfam 0.23076923076923078 nelle positive\n",
            "segnalet 0.76 nelle positive\n",
            "romantic 0.8461538461538461 nelle positive\n",
            "lavagg 0.24 nelle positive\n",
            "rapin 0.13636363636363635 nelle positive\n",
            "mat 0.7727272727272727 nelle positive\n",
            "aggir 0.15384615384615385 nelle positive\n",
            "presupporr 0.2 nelle positive\n",
            "pseud 0.041666666666666664 nelle positive\n",
            "scorrett 0.04 nelle positive\n",
            "bergam 0.8260869565217391 nelle positive\n",
            "tribunal 0.96 nelle positive\n",
            "concorrenzial 0.8846153846153846 nelle positive\n",
            "terrific 0.08695652173913043 nelle positive\n",
            "jet 0.7692307692307693 nelle positive\n",
            "briciol 0.125 nelle positive\n",
            "spiffer 0.12 nelle positive\n",
            "sbarr 0.22727272727272727 nelle positive\n",
            "ripetut 0.16666666666666666 nelle positive\n",
            "casaling 0.92 nelle positive\n",
            "sfortunat 0.24 nelle positive\n",
            "chiostr 0.782608695652174 nelle positive\n",
            "chalet 0.8181818181818182 nelle positive\n",
            "anatr 0.875 nelle positive\n",
            "laghett 0.8181818181818182 nelle positive\n",
            "veget 0.84 nelle positive\n",
            "muesl 0.8 nelle positive\n",
            "specializz 0.76 nelle positive\n",
            "efficientissim 0.96 nelle positive\n",
            "civitell 0.9090909090909091 nelle positive\n",
            "manovr 0.21739130434782608 nelle positive\n",
            "fontan 0.8333333333333334 nelle positive\n",
            "salin 0.875 nelle positive\n",
            "pattu 0.08695652173913043 nelle positive\n",
            "mignon 0.21739130434782608 nelle positive\n",
            "reazion 0.20833333333333334 nelle positive\n",
            "bocchett 0.12 nelle positive\n",
            "possess 0.16 nelle positive\n",
            "commed 0.8571428571428571 nelle positive\n",
            "montenapoleon 0.96 nelle positive\n",
            "tantomen 0.08 nelle positive\n",
            "allib 0.04 nelle positive\n",
            "biennal 0.7619047619047619 nelle positive\n",
            "maschil 0.19047619047619047 nelle positive\n",
            "consol 0.24 nelle positive\n",
            "cauzion 0.21739130434782608 nelle positive\n",
            "ping 0.8333333333333334 nelle positive\n",
            "compatt 0.8 nelle positive\n",
            "ovest 0.8260869565217391 nelle positive\n",
            "squill 0.20833333333333334 nelle positive\n",
            "legal 0.22727272727272727 nelle positive\n",
            "rud 0.16666666666666666 nelle positive\n",
            "regolament 0.19047619047619047 nelle positive\n",
            "perven 0.041666666666666664 nelle positive\n",
            "lacc 0.7727272727272727 nelle positive\n",
            "giudecc 0.9047619047619048 nelle positive\n",
            "prestissim 0.7916666666666666 nelle positive\n",
            "pomp 0.15 nelle positive\n",
            "sbrigat 0.16666666666666666 nelle positive\n",
            "usual 0.9166666666666666 nelle positive\n",
            "amiciz 0.85 nelle positive\n",
            "ski 0.8636363636363636 nelle positive\n",
            "maestos 0.7916666666666666 nelle positive\n",
            "caldai 0.14285714285714285 nelle positive\n",
            "alpin 0.8695652173913043 nelle positive\n",
            "sciistic 0.9047619047619048 nelle positive\n",
            "andarcen 0.08695652173913043 nelle positive\n",
            "gardon 0.8260869565217391 nelle positive\n",
            "salern 0.8260869565217391 nelle positive\n",
            "impens 0.125 nelle positive\n",
            "pick 0.8333333333333334 nelle positive\n",
            "souven 0.9166666666666666 nelle positive\n",
            "martell 0.08333333333333333 nelle positive\n",
            "sproposit 0.21739130434782608 nelle positive\n",
            "incur 0.16666666666666666 nelle positive\n",
            "disavventur 0.13636363636363635 nelle positive\n",
            "nemb 1.0 nelle positive\n",
            "impratic 0.041666666666666664 nelle positive\n",
            "ruzzin 1.0 nelle positive\n",
            "presep 0.8181818181818182 nelle positive\n",
            "torines 0.8636363636363636 nelle positive\n",
            "ise 0.8695652173913043 nelle positive\n",
            "eccezzional 0.9523809523809523 nelle positive\n",
            "muffin 0.8695652173913043 nelle positive\n",
            "cupol 1.0 nelle positive\n",
            "napoleon 0.8421052631578947 nelle positive\n",
            "invent 0.22727272727272727 nelle positive\n",
            "aragones 0.9047619047619048 nelle positive\n",
            "ampiezz 0.782608695652174 nelle positive\n",
            "fornitissim 0.9130434782608695 nelle positive\n",
            "instanc 0.7619047619047619 nelle positive\n",
            "ammass 0.18181818181818182 nelle positive\n",
            "capital 0.8695652173913043 nelle positive\n",
            "nespress 0.9565217391304348 nelle positive\n",
            "cris 0.21739130434782608 nelle positive\n",
            "palumb 0.7777777777777778 nelle positive\n",
            "nobil 0.8636363636363636 nelle positive\n",
            "simon 1.0 nelle positive\n",
            "ciottol 0.9545454545454546 nelle positive\n",
            "ebraic 0.8636363636363636 nelle positive\n",
            "nostalg 0.8260869565217391 nelle positive\n",
            "lussuregg 0.9130434782608695 nelle positive\n",
            "stip 0.18181818181818182 nelle positive\n",
            "contemp 0.9130434782608695 nelle positive\n",
            "affettu 1.0 nelle positive\n",
            "congratul 0.8260869565217391 nelle positive\n",
            "sardegn 0.9047619047619048 nelle positive\n",
            "accus 0.2 nelle positive\n",
            "lill 0.8695652173913043 nelle positive\n",
            "paradisiac 0.9047619047619048 nelle positive\n",
            "locazion 0.782608695652174 nelle positive\n",
            "eufem 0.21739130434782608 nelle positive\n",
            "touring 0.8260869565217391 nelle positive\n",
            "stracc 0.21739130434782608 nelle positive\n",
            "ingiall 0.0 nelle positive\n",
            "ospedalier 0.045454545454545456 nelle positive\n",
            "scol 0.13636363636363635 nelle positive\n",
            "posacen 0.2 nelle positive\n",
            "acid 0.15 nelle positive\n",
            "negoziett 0.7727272727272727 nelle positive\n",
            "inconcep 0.045454545454545456 nelle positive\n",
            "galles 0.9090909090909091 nelle positive\n",
            "prelib 0.9545454545454546 nelle positive\n",
            "facilit 0.7619047619047619 nelle positive\n",
            "cipoll 0.21052631578947367 nelle positive\n",
            "appag 0.8636363636363636 nelle positive\n",
            "pendenz 0.18181818181818182 nelle positive\n",
            "assecond 0.9090909090909091 nelle positive\n",
            "vignet 0.9545454545454546 nelle positive\n",
            "passerell 0.8095238095238095 nelle positive\n",
            "dargl 0.13636363636363635 nelle positive\n",
            "striminz 0.09523809523809523 nelle positive\n",
            "zepp 0.22727272727272727 nelle positive\n",
            "iron 0.2 nelle positive\n",
            "pest 0.2 nelle positive\n",
            "gugl 1.0 nelle positive\n",
            "orzar 0.19047619047619047 nelle positive\n",
            "calabr 0.7894736842105263 nelle positive\n",
            "chamois 0.8125 nelle positive\n",
            "zamp 0.8095238095238095 nelle positive\n",
            "passeggin 0.2222222222222222 nelle positive\n",
            "maniacal 0.9545454545454546 nelle positive\n",
            "fedel 0.7619047619047619 nelle positive\n",
            "aperol 0.9 nelle positive\n",
            "sconvolt 0.1 nelle positive\n",
            "incontamin 0.9545454545454546 nelle positive\n",
            "accumul 0.22727272727272727 nelle positive\n",
            "arrocc 0.7727272727272727 nelle positive\n",
            "gioiellin 0.9545454545454546 nelle positive\n",
            "sigill 0.13636363636363635 nelle positive\n",
            "magazzin 0.7619047619047619 nelle positive\n",
            "chateau 1.0 nelle positive\n",
            "monfort 1.0 nelle positive\n",
            "fattibil 0.7727272727272727 nelle positive\n",
            "pong 0.8636363636363636 nelle positive\n",
            "aprirl 0.18181818181818182 nelle positive\n",
            "cerv 0.8571428571428571 nelle positive\n",
            "site 1.0 nelle positive\n",
            "lacun 0.14285714285714285 nelle positive\n",
            "robert 0.9230769230769231 nelle positive\n",
            "dozzin 0.7619047619047619 nelle positive\n",
            "motoscaf 0.9473684210526315 nelle positive\n",
            "acar 0.09523809523809523 nelle positive\n",
            "croll 0.09523809523809523 nelle positive\n",
            "centrifug 0.8095238095238095 nelle positive\n",
            "patrimon 0.85 nelle positive\n",
            "andarmen 0.15789473684210525 nelle positive\n",
            "angioin 0.9523809523809523 nelle positive\n",
            "scadenz 0.19047619047619047 nelle positive\n",
            "svogl 0.1 nelle positive\n",
            "decim 0.8 nelle positive\n",
            "schifos 0.09523809523809523 nelle positive\n",
            "belved 0.8571428571428571 nelle positive\n",
            "paragg 0.9523809523809523 nelle positive\n",
            "turr 0.9473684210526315 nelle positive\n",
            "preconfezion 0.15 nelle positive\n",
            "crepes 0.95 nelle positive\n",
            "graditissim 1.0 nelle positive\n",
            "bistr 0.7894736842105263 nelle positive\n",
            "vicolett 0.8095238095238095 nelle positive\n",
            "storc 0.14285714285714285 nelle positive\n",
            "protett 0.7619047619047619 nelle positive\n",
            "maison 1.0 nelle positive\n",
            "bibliotec 1.0 nelle positive\n",
            "depliant 0.19047619047619047 nelle positive\n",
            "arcad 1.0 nelle positive\n",
            "senn 0.15789473684210525 nelle positive\n",
            "replic 0.14285714285714285 nelle positive\n",
            "daniel 0.8333333333333334 nelle positive\n",
            "indiz 0.23809523809523808 nelle positive\n",
            "suites 0.85 nelle positive\n",
            "duc 0.9473684210526315 nelle positive\n",
            "oscen 0.09523809523809523 nelle positive\n",
            "errat 0.05 nelle positive\n",
            "escrement 0.09523809523809523 nelle positive\n",
            "elegantissim 1.0 nelle positive\n",
            "grapp 0.7619047619047619 nelle positive\n",
            "lontanissim 0.14285714285714285 nelle positive\n",
            "condomin 0.2 nelle positive\n",
            "high 0.7619047619047619 nelle positive\n",
            "asol 0.8421052631578947 nelle positive\n",
            "maresc 0.7857142857142857 nelle positive\n",
            "padov 0.8095238095238095 nelle positive\n",
            "tubatur 0.23809523809523808 nelle positive\n",
            "enterpris 0.8888888888888888 nelle positive\n",
            "indiffert 0.047619047619047616 nelle positive\n",
            "serrand 0.15 nelle positive\n",
            "marketing 0.1111111111111111 nelle positive\n",
            "account 0.16666666666666666 nelle positive\n",
            "zza 0.8823529411764706 nelle positive\n",
            "main 0.8 nelle positive\n",
            "carc 0.05 nelle positive\n",
            "singl 0.8 nelle positive\n",
            "giustif 0.0 nelle positive\n",
            "loft 0.7647058823529411 nelle positive\n",
            "composizion 0.95 nelle positive\n",
            "designer 0.7894736842105263 nelle positive\n",
            "anticipat 0.05 nelle positive\n",
            "ramad 0.9333333333333333 nelle positive\n",
            "declass 0.0 nelle positive\n",
            "soccors 0.2 nelle positive\n",
            "manzon 0.8947368421052632 nelle positive\n",
            "cannavacciuol 0.8888888888888888 nelle positive\n",
            "brun 0.85 nelle positive\n",
            "bravur 0.85 nelle positive\n",
            "offens 0.10526315789473684 nelle positive\n",
            "maur 1.0 nelle positive\n",
            "medioeval 0.9 nelle positive\n",
            "spalanc 0.15 nelle positive\n",
            "ades 0.0 nelle positive\n",
            "conquist 0.9 nelle positive\n",
            "marmellatin 0.1 nelle positive\n",
            "gradevolissim 1.0 nelle positive\n",
            "tartuf 0.7894736842105263 nelle positive\n",
            "gregor 0.9 nelle positive\n",
            "panic 0.15 nelle positive\n",
            "cimbron 0.8125 nelle positive\n",
            "chinatown 1.0 nelle positive\n",
            "ike 0.2 nelle positive\n",
            "rasent 0.2 nelle positive\n",
            "inospital 0.15 nelle positive\n",
            "attrezzatissim 1.0 nelle positive\n",
            "felicissim 0.85 nelle positive\n",
            "driv 0.9473684210526315 nelle positive\n",
            "lattos 0.7777777777777778 nelle positive\n",
            "rottur 0.05263157894736842 nelle positive\n",
            "invan 0.2 nelle positive\n",
            "evenit 0.95 nelle positive\n",
            "tolt 0.15789473684210525 nelle positive\n",
            "gennar 0.9 nelle positive\n",
            "level 0.8666666666666667 nelle positive\n",
            "ridutt 0.9 nelle positive\n",
            "inadatt 0.2222222222222222 nelle positive\n",
            "avin 0.8888888888888888 nelle positive\n",
            "infil 0.2 nelle positive\n",
            "nemic 0.05263157894736842 nelle positive\n",
            "meridional 0.9473684210526315 nelle positive\n",
            "confer 0.7894736842105263 nelle positive\n",
            "sgabuzzin 0.05263157894736842 nelle positive\n",
            "lis 0.05263157894736842 nelle positive\n",
            "discaric 0.05555555555555555 nelle positive\n",
            "bistrot 1.0 nelle positive\n",
            "tetr 0.10526315789473684 nelle positive\n",
            "attentissim 0.9473684210526315 nelle positive\n",
            "scann 0.7647058823529411 nelle positive\n",
            "luxury 0.8888888888888888 nelle positive\n",
            "ciaspol 0.8888888888888888 nelle positive\n",
            "caminett 0.9473684210526315 nelle positive\n",
            "limousin 0.8421052631578947 nelle positive\n",
            "carlos 0.8571428571428571 nelle positive\n",
            "horror 0.0 nelle positive\n",
            "sien 0.8947368421052632 nelle positive\n",
            "starc 0.10526315789473684 nelle positive\n",
            "accredit 0.11764705882352941 nelle positive\n",
            "offes 0.0 nelle positive\n",
            "rarit 0.9375 nelle positive\n",
            "monz 0.8 nelle positive\n",
            "dolcezz 0.8947368421052632 nelle positive\n",
            "giu 0.16666666666666666 nelle positive\n",
            "atterragg 0.8823529411764706 nelle positive\n",
            "rufol 1.0 nelle positive\n",
            "divertit 0.8947368421052632 nelle positive\n",
            "rezzon 0.8947368421052632 nelle positive\n",
            "gazeb 0.7647058823529411 nelle positive\n",
            "customer 0.16666666666666666 nelle positive\n",
            "mergellin 1.0 nelle positive\n",
            "calcin 0.9411764705882353 nelle positive\n",
            "esposizion 0.8421052631578947 nelle positive\n",
            "multilingu 0.9473684210526315 nelle positive\n",
            "nicolaus 0.9333333333333333 nelle positive\n",
            "inusual 1.0 nelle positive\n",
            "ignar 0.21052631578947367 nelle positive\n",
            "armen 0.8947368421052632 nelle positive\n",
            "scrupol 0.7894736842105263 nelle positive\n",
            "lugubr 0.21052631578947367 nelle positive\n",
            "sommelier 0.8947368421052632 nelle positive\n",
            "paesaggist 0.8947368421052632 nelle positive\n",
            "magliett 0.2222222222222222 nelle positive\n",
            "ricercatezz 0.7894736842105263 nelle positive\n",
            "florid 0.7777777777777778 nelle positive\n",
            "costegg 0.8333333333333334 nelle positive\n",
            "decoder 0.2222222222222222 nelle positive\n",
            "nipot 1.0 nelle positive\n",
            "depandanc 0.17647058823529413 nelle positive\n",
            "impossibilit 0.05555555555555555 nelle positive\n",
            "serg 0.8235294117647058 nelle positive\n",
            "formos 1.0 nelle positive\n",
            "bisanz 0.9285714285714286 nelle positive\n",
            "orto 0.9473684210526315 nelle positive\n",
            "emett 0.15789473684210525 nelle positive\n",
            "avventor 0.23529411764705882 nelle positive\n",
            "intermin 0.16666666666666666 nelle positive\n",
            "casamicciol 0.7777777777777778 nelle positive\n",
            "condiscend 0.0 nelle positive\n",
            "vittim 0.2222222222222222 nelle positive\n",
            "kneipp 0.7777777777777778 nelle positive\n",
            "open 0.7777777777777778 nelle positive\n",
            "precision 0.7777777777777778 nelle positive\n",
            "capient 0.8888888888888888 nelle positive\n",
            "acet 0.0625 nelle positive\n",
            "azzurr 0.8333333333333334 nelle positive\n",
            "tonnell 0.7777777777777778 nelle positive\n",
            "gonf 0.16666666666666666 nelle positive\n",
            "estes 0.8235294117647058 nelle positive\n",
            "altru 0.1111111111111111 nelle positive\n",
            "angur 0.2 nelle positive\n",
            "gar 0.7777777777777778 nelle positive\n",
            "caserm 0.11764705882352941 nelle positive\n",
            "extracomunitar 0.17647058823529413 nelle positive\n",
            "intrappol 0.06666666666666667 nelle positive\n",
            "provatel 0.8888888888888888 nelle positive\n",
            "disonest 0.0 nelle positive\n",
            "educatissim 0.9411764705882353 nelle positive\n",
            "inefficient 0.058823529411764705 nelle positive\n",
            "town 0.8125 nelle positive\n",
            "furor 1.0 nelle positive\n",
            "tendenz 0.8333333333333334 nelle positive\n",
            "by 0.7647058823529411 nelle positive\n",
            "tiramisù 0.8823529411764706 nelle positive\n",
            "unto 0.16666666666666666 nelle positive\n",
            "prevent 0.17647058823529413 nelle positive\n",
            "inaccess 0.0 nelle positive\n",
            "silicon 0.058823529411764705 nelle positive\n",
            "gocciol 0.0 nelle positive\n",
            "lentezz 0.16666666666666666 nelle positive\n",
            "eataly 0.9411764705882353 nelle positive\n",
            "concret 0.7777777777777778 nelle positive\n",
            "coner 0.8 nelle positive\n",
            "topai 0.16666666666666666 nelle positive\n",
            "indefin 0.2222222222222222 nelle positive\n",
            "gluten 0.875 nelle positive\n",
            "arsenal 1.0 nelle positive\n",
            "dormitor 0.23529411764705882 nelle positive\n",
            "familiar 0.8888888888888888 nelle positive\n",
            "rinascimental 0.8333333333333334 nelle positive\n",
            "molven 0.9375 nelle positive\n",
            "sauz 0.9333333333333333 nelle positive\n",
            "gamberett 0.23529411764705882 nelle positive\n",
            "great 0.8333333333333334 nelle positive\n",
            "bonadies 0.7692307692307693 nelle positive\n",
            "pip 0.1111111111111111 nelle positive\n",
            "glamour 0.8888888888888888 nelle positive\n",
            "persist 0.0 nelle positive\n",
            "vastissim 0.9444444444444444 nelle positive\n",
            "piemontes 0.8235294117647058 nelle positive\n",
            "precar 0.0 nelle positive\n",
            "circumvesuv 0.875 nelle positive\n",
            "motorin 0.23529411764705882 nelle positive\n",
            "transazion 0.06666666666666667 nelle positive\n",
            "manutentor 0.125 nelle positive\n",
            "tropical 0.8235294117647058 nelle positive\n",
            "ces 0.7692307692307693 nelle positive\n",
            "less 0.17647058823529413 nelle positive\n",
            "fargl 0.23529411764705882 nelle positive\n",
            "ritm 0.7647058823529411 nelle positive\n",
            "ritz 0.7647058823529411 nelle positive\n",
            "cucchiain 0.058823529411764705 nelle positive\n",
            "coniug 0.8235294117647058 nelle positive\n",
            "straf 0.9166666666666666 nelle positive\n",
            "montec 0.8461538461538461 nelle positive\n",
            "torrid 0.11764705882352941 nelle positive\n",
            "inqualific 0.0 nelle positive\n",
            "clor 0.17647058823529413 nelle positive\n",
            "opulent 1.0 nelle positive\n",
            "montefalc 0.9375 nelle positive\n",
            "racchiud 0.8235294117647058 nelle positive\n",
            "chimic 0.0625 nelle positive\n",
            "guggenheim 0.8235294117647058 nelle positive\n",
            "atvo 1.0 nelle positive\n",
            "scricchiol 0.11764705882352941 nelle positive\n",
            "golos 0.8235294117647058 nelle positive\n",
            "alabardier 1.0 nelle positive\n",
            "indisporr 0.0 nelle positive\n",
            "indegn 0.0 nelle positive\n",
            "cavol 0.058823529411764705 nelle positive\n",
            "multipl 0.7647058823529411 nelle positive\n",
            "altin 0.9411764705882353 nelle positive\n",
            "bait 0.7857142857142857 nelle positive\n",
            "acciai 0.1875 nelle positive\n",
            "kett 0.9285714285714286 nelle positive\n",
            "somigl 0.23529411764705882 nelle positive\n",
            "gallipol 0.8125 nelle positive\n",
            "chè 0.11764705882352941 nelle positive\n",
            "esplicit 0.17647058823529413 nelle positive\n",
            "sandr 1.0 nelle positive\n",
            "minuz 1.0 nelle positive\n",
            "preparatissim 1.0 nelle positive\n",
            "finlandes 0.875 nelle positive\n",
            "consunt 0.058823529411764705 nelle positive\n",
            "obiett 0.23529411764705882 nelle positive\n",
            "prigion 0.1875 nelle positive\n",
            "heid 0.8235294117647058 nelle positive\n",
            "james 0.7647058823529411 nelle positive\n",
            "ingrass 0.8235294117647058 nelle positive\n",
            "novecent 0.8571428571428571 nelle positive\n",
            "bened 0.8235294117647058 nelle positive\n",
            "senes 1.0 nelle positive\n",
            "stagn 0.0625 nelle positive\n",
            "capalb 0.9230769230769231 nelle positive\n",
            "festiv 0.8125 nelle positive\n",
            "amat 0.8125 nelle positive\n",
            "licenz 0.23529411764705882 nelle positive\n",
            "brunell 1.0 nelle positive\n",
            "boat 0.8 nelle positive\n",
            "capitan 1.0 nelle positive\n",
            "pp 0.8461538461538461 nelle positive\n",
            "recentissim 0.8823529411764706 nelle positive\n",
            "fiab 0.8235294117647058 nelle positive\n",
            "stufett 0.058823529411764705 nelle positive\n",
            "encom 0.9411764705882353 nelle positive\n",
            "abat 0.21428571428571427 nelle positive\n",
            "assegnatam 0.17647058823529413 nelle positive\n",
            "idilliac 0.7647058823529411 nelle positive\n",
            "mendic 0.125 nelle positive\n",
            "carrefour 0.875 nelle positive\n",
            "chiazz 0.13333333333333333 nelle positive\n",
            "emil 1.0 nelle positive\n",
            "j 0.7692307692307693 nelle positive\n",
            "stanzett 0.125 nelle positive\n",
            "bombonier 0.9375 nelle positive\n",
            "accatast 0.0 nelle positive\n",
            "milit 0.0625 nelle positive\n",
            "sporg 0.1875 nelle positive\n",
            "mozzicon 0.125 nelle positive\n",
            "carabin 0.0 nelle positive\n",
            "panetter 0.875 nelle positive\n",
            "esos 0.1875 nelle positive\n",
            "ottant 0.1875 nelle positive\n",
            "toss 0.0625 nelle positive\n",
            "beff 0.0 nelle positive\n",
            "petal 0.8125 nelle positive\n",
            "urtar 0.2 nelle positive\n",
            "disinfett 0.07692307692307693 nelle positive\n",
            "irrespir 0.0 nelle positive\n",
            "inappunt 0.9375 nelle positive\n",
            "splend 0.875 nelle positive\n",
            "courmayeur 1.0 nelle positive\n",
            "giovanil 0.9375 nelle positive\n",
            "blatt 0.0 nelle positive\n",
            "doc 0.8125 nelle positive\n",
            "infiltr 0.125 nelle positive\n",
            "maraton 0.7692307692307693 nelle positive\n",
            "bruf 0.9285714285714286 nelle positive\n",
            "caraib 1.0 nelle positive\n",
            "declin 0.1875 nelle positive\n",
            "insoddisfacent 0.0 nelle positive\n",
            "sorrentin 0.8125 nelle positive\n",
            "tugur 0.0 nelle positive\n",
            "restaurant 0.8 nelle positive\n",
            "anta 0.0625 nelle positive\n",
            "stall 0.0625 nelle positive\n",
            "congestion 0.8125 nelle positive\n",
            "diversific 0.8666666666666667 nelle positive\n",
            "dinam 1.0 nelle positive\n",
            "ia 0.8 nelle positive\n",
            "calorifer 0.0625 nelle positive\n",
            "puff 0.125 nelle positive\n",
            "malconc 0.07142857142857142 nelle positive\n",
            "areazion 0.2 nelle positive\n",
            "sollec 0.1875 nelle positive\n",
            "zatter 0.8125 nelle positive\n",
            "raviol 0.8 nelle positive\n",
            "gustosissim 1.0 nelle positive\n",
            "sbuff 0.07142857142857142 nelle positive\n",
            "david 0.875 nelle positive\n",
            "spig 0.875 nelle positive\n",
            "jazz 0.875 nelle positive\n",
            "affor 0.9285714285714286 nelle positive\n",
            "utensil 0.23076923076923078 nelle positive\n",
            "schiavon 1.0 nelle positive\n",
            "cucinott 0.9333333333333333 nelle positive\n",
            "normalissim 0.2 nelle positive\n",
            "bonific 0.0 nelle positive\n",
            "correr 0.8333333333333334 nelle positive\n",
            "inappropr 0.0625 nelle positive\n",
            "tech 0.7857142857142857 nelle positive\n",
            "candeggin 0.0 nelle positive\n",
            "sabbios 0.8 nelle positive\n",
            "impregn 0.13333333333333333 nelle positive\n",
            "parm 0.9333333333333333 nelle positive\n",
            "inaffid 0.13333333333333333 nelle positive\n",
            "contegg 0.23076923076923078 nelle positive\n",
            "sproporzion 0.06666666666666667 nelle positive\n",
            "titub 0.8666666666666667 nelle positive\n",
            "frenes 1.0 nelle positive\n",
            "imbrogl 0.06666666666666667 nelle positive\n",
            "fognatur 0.06666666666666667 nelle positive\n",
            "vomit 0.07142857142857142 nelle positive\n",
            "telai 0.06666666666666667 nelle positive\n",
            "scroll 0.13333333333333333 nelle positive\n",
            "spiaccic 0.07142857142857142 nelle positive\n",
            "precipit 0.2 nelle positive\n",
            "nan 0.13333333333333333 nelle positive\n",
            "ripostigl 0.23076923076923078 nelle positive\n",
            "doubletre 0.8181818181818182 nelle positive\n",
            "marmor 0.9230769230769231 nelle positive\n",
            "ustion 0.13333333333333333 nelle positive\n",
            "routin 0.8 nelle positive\n",
            "big 0.8571428571428571 nelle positive\n",
            "vasett 0.2 nelle positive\n",
            "incompett 0.0 nelle positive\n",
            "principess 1.0 nelle positive\n",
            "interpret 0.8 nelle positive\n",
            "rang 0.8 nelle positive\n",
            "burber 0.23076923076923078 nelle positive\n",
            "innervos 0.14285714285714285 nelle positive\n",
            "nic 0.9230769230769231 nelle positive\n",
            "brac 0.9285714285714286 nelle positive\n",
            "abus 0.2 nelle positive\n",
            "affil 0.14285714285714285 nelle positive\n",
            "bidon 0.2 nelle positive\n",
            "scongel 0.13333333333333333 nelle positive\n",
            "nauseabond 0.06666666666666667 nelle positive\n",
            "cess 0.2 nelle positive\n",
            "deters 0.13333333333333333 nelle positive\n",
            "trap 0.7692307692307693 nelle positive\n",
            "compiac 0.8666666666666667 nelle positive\n",
            "spolver 0.07142857142857142 nelle positive\n",
            "sciator 0.7692307692307693 nelle positive\n",
            "liquor 0.7857142857142857 nelle positive\n",
            "lampegg 0.16666666666666666 nelle positive\n",
            "gadget 0.7857142857142857 nelle positive\n",
            "salatissim 0.06666666666666667 nelle positive\n",
            "inspieg 0.2 nelle positive\n",
            "crocett 0.9333333333333333 nelle positive\n",
            "infim 0.06666666666666667 nelle positive\n",
            "intermittt 0.07142857142857142 nelle positive\n",
            "alon 0.06666666666666667 nelle positive\n",
            "litoral 0.9333333333333333 nelle positive\n",
            "cabinov 0.7857142857142857 nelle positive\n",
            "pensioncin 0.13333333333333333 nelle positive\n",
            "malridott 0.13333333333333333 nelle positive\n",
            "convention 0.9333333333333333 nelle positive\n",
            "primissim 0.8666666666666667 nelle positive\n",
            "jour 0.08333333333333333 nelle positive\n",
            "bandin 0.8181818181818182 nelle positive\n",
            "indign 0.06666666666666667 nelle positive\n",
            "eli 1.0 nelle positive\n",
            "vistos 0.14285714285714285 nelle positive\n",
            "profumat 0.14285714285714285 nelle positive\n",
            "mir 0.7857142857142857 nelle positive\n",
            "posticin 0.9285714285714286 nelle positive\n",
            "scolor 0.14285714285714285 nelle positive\n",
            "alpi 0.8571428571428571 nelle positive\n",
            "pic 1.0 nelle positive\n",
            "tacchin 0.7857142857142857 nelle positive\n",
            "quid 0.9285714285714286 nelle positive\n",
            "sciatt 0.14285714285714285 nelle positive\n",
            "coreograf 0.7857142857142857 nelle positive\n",
            "rossett 0.0 nelle positive\n",
            "trev 1.0 nelle positive\n",
            "stilist 0.8461538461538461 nelle positive\n",
            "missor 0.8571428571428571 nelle positive\n",
            "oltrem 0.8571428571428571 nelle positive\n",
            "connub 1.0 nelle positive\n",
            "tront 1.0 nelle positive\n",
            "sard 0.8461538461538461 nelle positive\n",
            "hub 1.0 nelle positive\n",
            "cumul 0.21428571428571427 nelle positive\n",
            "fuorigrott 0.9285714285714286 nelle positive\n",
            "orchestr 0.9230769230769231 nelle positive\n",
            "chiosc 0.9230769230769231 nelle positive\n",
            "entroterr 0.7857142857142857 nelle positive\n",
            "distrutt 0.14285714285714285 nelle positive\n",
            "limitatissim 0.14285714285714285 nelle positive\n",
            "ostil 0.15384615384615385 nelle positive\n",
            "zunic 1.0 nelle positive\n",
            "oulx 0.9166666666666666 nelle positive\n",
            "cavour 0.7692307692307693 nelle positive\n",
            "verucc 0.9230769230769231 nelle positive\n",
            "inaud 0.07142857142857142 nelle positive\n",
            "monitor 0.21428571428571427 nelle positive\n",
            "erron 0.21428571428571427 nelle positive\n",
            "applaus 0.8461538461538461 nelle positive\n",
            "imperial 0.7857142857142857 nelle positive\n",
            "ivan 0.8571428571428571 nelle positive\n",
            "liofilizz 0.0 nelle positive\n",
            "banchin 0.7857142857142857 nelle positive\n",
            "scolleg 0.21428571428571427 nelle positive\n",
            "comprensor 0.7692307692307693 nelle positive\n",
            "drastic 0.21428571428571427 nelle positive\n",
            "riv 0.8461538461538461 nelle positive\n",
            "solert 0.9285714285714286 nelle positive\n",
            "boh 0.21428571428571427 nelle positive\n",
            "sinton 0.9285714285714286 nelle positive\n",
            "furb 0.07142857142857142 nelle positive\n",
            "nicol 1.0 nelle positive\n",
            "canalett 0.7857142857142857 nelle positive\n",
            "asma 0.14285714285714285 nelle positive\n",
            "alexandr 0.9166666666666666 nelle positive\n",
            "bah 0.0 nelle positive\n",
            "annual 0.7857142857142857 nelle positive\n",
            "creat 0.9285714285714286 nelle positive\n",
            "agghiacc 0.14285714285714285 nelle positive\n",
            "croccant 0.7692307692307693 nelle positive\n",
            "rattopp 0.0 nelle positive\n",
            "vulc 0.9230769230769231 nelle positive\n",
            "asso 0.23076923076923078 nelle positive\n",
            "andarsen 0.23076923076923078 nelle positive\n",
            "dietet 0.7857142857142857 nelle positive\n",
            "faraglion 0.9285714285714286 nelle positive\n",
            "traged 0.07142857142857142 nelle positive\n",
            "aggrav 0.07142857142857142 nelle positive\n",
            "inag 0.14285714285714285 nelle positive\n",
            "barbon 0.23076923076923078 nelle positive\n",
            "gianfranc 0.9285714285714286 nelle positive\n",
            "carmin 0.8571428571428571 nelle positive\n",
            "genial 0.8461538461538461 nelle positive\n",
            "gravissim 0.07692307692307693 nelle positive\n",
            "crystal 0.8 nelle positive\n",
            "insuper 1.0 nelle positive\n",
            "straconsigl 1.0 nelle positive\n",
            "sbors 0.0 nelle positive\n",
            "grill 0.7692307692307693 nelle positive\n",
            "illegal 0.0 nelle positive\n",
            "rinasc 0.9230769230769231 nelle positive\n",
            "incomprens 0.15384615384615385 nelle positive\n",
            "cer 0.18181818181818182 nelle positive\n",
            "stupefatt 1.0 nelle positive\n",
            "igen 0.0 nelle positive\n",
            "coop 0.8333333333333334 nelle positive\n",
            "sottilett 0.0 nelle positive\n",
            "uptown 0.8333333333333334 nelle positive\n",
            "gemm 0.9230769230769231 nelle positive\n",
            "fessur 0.23076923076923078 nelle positive\n",
            "magistral 1.0 nelle positive\n",
            "solar 0.8333333333333334 nelle positive\n",
            "vivald 0.9230769230769231 nelle positive\n",
            "xvii 0.8461538461538461 nelle positive\n",
            "party 0.7692307692307693 nelle positive\n",
            "amabil 0.8461538461538461 nelle positive\n",
            "tom 0.7692307692307693 nelle positive\n",
            "instabil 0.23076923076923078 nelle positive\n",
            "immanc 0.8461538461538461 nelle positive\n",
            "brandin 0.08333333333333333 nelle positive\n",
            "salient 0.8461538461538461 nelle positive\n",
            "affiss 0.15384615384615385 nelle positive\n",
            "vincitor 0.9230769230769231 nelle positive\n",
            "ricchezz 0.7692307692307693 nelle positive\n",
            "smont 0.07692307692307693 nelle positive\n",
            "ineguagl 0.8461538461538461 nelle positive\n",
            "non 0.5303771783241824 nelle positive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "kF1ZuLdbDiuK",
        "outputId": "6ccb696f-e056-46b6-de6c-d60f23a9a384",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        }
      },
      "source": [
        "#faccio il wordcloud\n",
        "lista_wordcloud2=[word for word in list_all_tokenized_lemmed_wordscloud if word in words]\n",
        "stringa_wordcloud2=' '.join(lista_wordcloud2)\n",
        "plt.imshow( WordCloud(collocations=False, stopwords=[], max_font_size=100, max_words=100, background_color=\"white\", random_state=666).generate(stringa_wordcloud2), \n",
        "            interpolation='bilinear')"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f9f3292b9e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6QQclhFCuRLOqvt9Avaot4diKHv4iqNGrq78KKk7HRhyyg5JxASFc01L6dD3o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MT0nBjbzUJTFVojQdyGzoWxJMlMgdZr2D2XJlNM1cnpL2vfy1lJSvmGEKLzusVPAw/M/vsvgdeo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3SmRLZUZzGWqCfry6xng6zXS5gOM4hAwP6IK438dUJoehKQhTYmdHPfXxMLqisK2+lgODI1RF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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "55aoKZXwEK2z",
        "colab_type": "text"
      },
      "source": [
        "NGRAM SUI LEMMAS (uni e bi, con discorso sul 'non')"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kLhPWllHEOxw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "total_df['stringa_text_tokenized_lemmed']=total_df['text_tokenized_lemmed'].apply(lambda x: ' '.join(x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xq_uXcFTEpCv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def do_ngrams(stringa_review):\n",
        "  # Break sentence in the token, remove empty tokens\n",
        "  tokenss = [tokenn for tokenn in stringa_review.split(\" \") if tokenn != \"\"]    \n",
        "  # Use the zip function to help us generate n-grams\n",
        "  # Concatentate the tokens into ngrams and return\n",
        "  ngrams = zip(*[tokenss[i:] for i in range(2)])\n",
        "  lista_di_tuple=list(ngrams)\n",
        "\n",
        "  lista_ngrams=[]\n",
        "  neg_found=False\n",
        "  for i in range (len(lista_di_tuple)):\n",
        "    if(lista_di_tuple[i][0]=='non'):\n",
        "      lista_ngrams.append(lista_di_tuple[i][0]+lista_di_tuple[i][1])\n",
        "      neg_found=True\n",
        "    else:\n",
        "      if neg_found==True:\n",
        "        #lista_ngrams.append(lista_di_tuple[i][0]+lista_di_tuple[i][1]) #da provare con o senza\n",
        "        neg_found=False\n",
        "      else:\n",
        "        lista_ngrams.append(lista_di_tuple[i][0])\n",
        "        lista_ngrams.append(lista_di_tuple[i][0]+lista_di_tuple[i][1])\n",
        "        neg_found=False\n",
        "\n",
        "  if neg_found==False:\n",
        "    lista_ngrams.append(lista_di_tuple[len(lista_di_tuple)-1][1])\n",
        "  \n",
        "  return ' '.join(lista_ngrams)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GbbHMjMeFxvK",
        "colab_type": "code",
        "outputId": "38d4f8f2-8c4e-4a95-dcff-d93af0b4ae63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "do_ngrams(\"ciao io non consiglio mai\")"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'ciao ciaoio io ionon nonconsiglio mai'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "srlvBuzlE8Yh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "total_df['stringa_ngrams']=total_df['stringa_text_tokenized_lemmed'].apply(do_ngrams)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lUUWDETFkkM0",
        "colab_type": "text"
      },
      "source": [
        "AGGIUNGO EMOTICONS E PUNTEGGIATURA IMPORTANTE AGLI NGRAMS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9_yor1wGS4Fy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def add_punct_to_ngrams(row):\n",
        "  stringa_ngrams=row['stringa_ngrams']\n",
        "  regex_puntoesc=re.compile('\\w![^!]') #perchè ho visto no correlaz tra aumentare i ! e aumentare positività \n",
        "  count_puntoesc=len(regex_puntoesc.findall(row['text']))\n",
        "  for i in range(count_puntoesc):\n",
        "    stringa_ngrams=stringa_ngrams+\" !\"\n",
        "\n",
        "  regex_puntointerr=re.compile('\\?') #perchè ho visto correlaz tra aumentare i ? e aumentare negatività\n",
        "  count_puntointerr=len(regex_puntointerr.findall(row['text']))\n",
        "  for i in range(count_puntointerr):\n",
        "    stringa_ngrams=stringa_ngrams+\" ?\"\n",
        "\n",
        "  return stringa_ngrams\n",
        "\n",
        "\n",
        "def add_emoji_to_ngrams(row):\n",
        "  stringa_ngrams=row['stringa_ngrams']\n",
        "  regex_good=re.compile(pos_emoticons_regex+'|'+pos_emojis_regex)\n",
        "  \n",
        "  count_good=regex_good.findall(row['text'])\n",
        "  for i in range(len(count_good)):\n",
        "    stringa_ngrams=stringa_ngrams+\" faccinafelice123\"\n",
        "\n",
        "  regex_bad=re.compile(neg_emoticons_regex+'|'+neg_emojis_regex)\n",
        "  count_bad=regex_bad.findall(row['text'])\n",
        "  for i in range(len(count_bad)):\n",
        "    stringa_ngrams=stringa_ngrams+\" faccinatriste123\"\n",
        "\n",
        "  return stringa_ngrams"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gjRYn8ofRXoH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#aggiungo qualche punteggiatura (scelta vedendo le statistiche)\n",
        "#nella stringa_ngrams\n",
        "total_df['stringa_ngrams']=total_df.apply(add_punct_to_ngrams, axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQzk6fX3b1_3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#tutte le emticons/emoji positive le codifico con faccinafelice123\n",
        "#quelle negative con faccinatriste123\n",
        "total_df['stringa_ngrams']=total_df.apply(add_emoji_to_ngrams, axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BfHgY_aNb3TJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#total_df.iloc[27].loc['text']  snippet per accedere ad una cella specifica"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kJW0vWEIiqtF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4a9966b5-85e6-42b8-fa80-49780b296aa5"
      },
      "source": [
        "total_df"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>class</th>\n",
              "      <th>has_pos_emoj_emot</th>\n",
              "      <th>has_neg_emoj_emot</th>\n",
              "      <th>text_tokenized_lemmed</th>\n",
              "      <th>stringa_text_tokenized_lemmed</th>\n",
              "      <th>stringa_ngrams</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Non è l'hotel più lussuoso in cui abbia mai so...</td>\n",
              "      <td>pos</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>[non, hotel, lussuos, mai, soggiorn, esperit, ...</td>\n",
              "      <td>non hotel lussuos mai soggiorn esperit comples...</td>\n",
              "      <td>nonhotel lussuos lussuosmai mai maisoggiorn so...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Siamo stati qui per 1 notte prima della nostra...</td>\n",
              "      <td>pos</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>[stat, nott, prim, sinistr, crocier, venez, pe...</td>\n",
              "      <td>stat nott prim sinistr crocier venez personal ...</td>\n",
              "      <td>stat statnott nott nottprim prim primsinistr s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hotel è ben posizionato per visitare Torino. A...</td>\n",
              "      <td>pos</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>[hotel, ben, posizion, visit, torin, circ, m, ...</td>\n",
              "      <td>hotel ben posizion visit torin circ m ferm aut...</td>\n",
              "      <td>hotel hotelben ben benposizion posizion posizi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>All'arrivo la cordialità e disponibilità dello...</td>\n",
              "      <td>pos</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>[arriv, cordial, disponibil, staff, qualit, ca...</td>\n",
              "      <td>arriv cordial disponibil staff qualit camer al...</td>\n",
              "      <td>arriv arrivcordial cordial cordialdisponibil d...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Abbiamo soggiornato per due notti alla fine de...</td>\n",
              "      <td>pos</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>[soggiorn, due, nott, fin, crocier, personal, ...</td>\n",
              "      <td>soggiorn due nott fin crocier personal molt co...</td>\n",
              "      <td>soggiorn soggiorndue due duenott nott nottfin ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41072</th>\n",
              "      <td>A due passi dal bioparco zoom di Torino, abbia...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>[due, pass, bioparc, zoom, torin, dorm, nott, ...</td>\n",
              "      <td>due pass bioparc zoom torin dorm nott grazios ...</td>\n",
              "      <td>due duepass pass passbioparc bioparc bioparczo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41073</th>\n",
              "      <td>Non esiste lobby ma si fa il check in in auto ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>[non, esist, lobby, far, check, aut, rec, dire...</td>\n",
              "      <td>non esist lobby far check aut rec dirett camer...</td>\n",
              "      <td>nonesist lobby lobbyfar far farcheck check che...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41074</th>\n",
              "      <td>Quattro giorni paese e paesaggi stupendi. Rapp...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>[quattr, giorn, paes, paesagg, stup, rapport, ...</td>\n",
              "      <td>quattr giorn paes paesagg stup rapport qualit ...</td>\n",
              "      <td>quattr quattrgiorn giorn giornpaes paes paespa...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41075</th>\n",
              "      <td>A parte la posizione non riesco a trovare alcu...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>[part, posizion, non, riusc, trov, alcun, punt...</td>\n",
              "      <td>part posizion non riusc trov alcun punt posit ...</td>\n",
              "      <td>part partposizion posizion posizionnon nonrius...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41076</th>\n",
              "      <td>Ho prenotato una camera privata, ed è stato mo...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>[prenot, camer, priv, star, molt, bell, sent, ...</td>\n",
              "      <td>prenot camer priv star molt bell sent hotel pr...</td>\n",
              "      <td>prenot prenotcamer camer camerpriv priv privst...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>41077 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    text  ...                                     stringa_ngrams\n",
              "0      Non è l'hotel più lussuoso in cui abbia mai so...  ...  nonhotel lussuos lussuosmai mai maisoggiorn so...\n",
              "1      Siamo stati qui per 1 notte prima della nostra...  ...  stat statnott nott nottprim prim primsinistr s...\n",
              "2      Hotel è ben posizionato per visitare Torino. A...  ...  hotel hotelben ben benposizion posizion posizi...\n",
              "3      All'arrivo la cordialità e disponibilità dello...  ...  arriv arrivcordial cordial cordialdisponibil d...\n",
              "4      Abbiamo soggiornato per due notti alla fine de...  ...  soggiorn soggiorndue due duenott nott nottfin ...\n",
              "...                                                  ...  ...                                                ...\n",
              "41072  A due passi dal bioparco zoom di Torino, abbia...  ...  due duepass pass passbioparc bioparc bioparczo...\n",
              "41073  Non esiste lobby ma si fa il check in in auto ...  ...  nonesist lobby lobbyfar far farcheck check che...\n",
              "41074  Quattro giorni paese e paesaggi stupendi. Rapp...  ...  quattr quattrgiorn giorn giornpaes paes paespa...\n",
              "41075  A parte la posizione non riesco a trovare alcu...  ...  part partposizion posizion posizionnon nonrius...\n",
              "41076  Ho prenotato una camera privata, ed è stato mo...  ...  prenot prenotcamer camer camerpriv priv privst...\n",
              "\n",
              "[41077 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cB_JVcMqFWE-",
        "colab_type": "text"
      },
      "source": [
        "VETTORIZZAZIONE DEL DATAFRAME (FIT SOLO SUI LABELLED)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RcjL16tgFXf8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vectorizer = TfidfVectorizer(input='content', encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, \n",
        "                             analyzer='word',\n",
        "                             stop_words=None,\n",
        "                             ngram_range=(1,1),\n",
        "                             max_df=0.9, min_df=5, max_features=None,\n",
        "                             vocabulary=None, binary=False, \n",
        "                             norm='l2', \n",
        "                             use_idf=True, smooth_idf=True, \n",
        "                             sublinear_tf=False)\n",
        "vectorizer = vectorizer.fit(total_df['stringa_ngrams'][:number_of_labelled]) #traino vocabolario solo sul training set\n",
        "#perchè non mi interessa vedere anche l'evaluation set perchè non so le label, non ha senso ad es calcolare la min e max df anche su quel set\n",
        "\n",
        "sparse_matrix=vectorizer.transform(total_df['stringa_ngrams'])\n",
        "#sparse matrix con row documenti e colonne le features create (il cui indice lo ricavo nel vocabulary)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_ubQoo5lUBm",
        "colab_type": "code",
        "outputId": "fdf4a522-768c-453f-886a-0c88332c3aa8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "vectorizer.vocabulary_.get(\"hotelnon\") #snippet per vedere se un ngrams è preso alla fine"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23787"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PeoZHN9EE1QK",
        "colab": {}
      },
      "source": [
        "def dump_to_file(file, labels):\n",
        "    with open(file, 'w', encoding='utf-8', newline='') as writeFile:\n",
        "        writer = csv.writer(writeFile)\n",
        "        writer.writerow(['Id', 'Predicted'])\n",
        "        writer.writerows(enumerate(labels))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IJFXXf3SbF2i",
        "colab_type": "text"
      },
      "source": [
        "LinearSVC "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WQ1tW-sYdJ-h",
        "colab": {}
      },
      "source": [
        "from sklearn.svm import LinearSVC\n",
        "classifier=LinearSVC(class_weight='balanced')\n",
        "classifier.fit(sparse_matrix[:number_of_labelled], total_df['class'][:number_of_labelled])\n",
        "y_pred=classifier.predict(sparse_matrix)\n",
        "\n",
        "dump_to_file('res.csv', y_pred[number_of_labelled:])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E4bUHSrA-RgV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "d8d9a49c-2a04-4f35-8a10-701a56b05dec"
      },
      "source": [
        "sparse_matrix.shape"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(41077, 59319)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MnBgv-QADUyn",
        "colab_type": "text"
      },
      "source": [
        "CROSS VALIDATION"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "i3LUVAqEak2K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6bf58cdc-ebdd-4998-afed-1cc6b8386620"
      },
      "source": [
        "from sklearn.model_selection import ParameterGrid, train_test_split\n",
        "from sklearn.metrics import f1_score\n",
        "\n",
        "y_true=total_df['class'][:number_of_labelled]\n",
        "\n",
        "params = {\n",
        "\"tol\":[1e-2, 1e-3, 1e-4,1e-6,1e-7],\n",
        "\"C\":[0.5,1.0,2.0,3.0],\n",
        "\"fit_intercept\": [True,False],\n",
        "'max_iter':[1000,3000]\n",
        "}\n",
        "\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "X_train_valid, X_test, y_train_valid, y_test=train_test_split(sparse_matrix[:number_of_labelled], np.array(y_true),\n",
        "                                                              stratify=np.array(y_true))\n",
        "\n",
        "# stratify fa in modo di ottenere la stessa distribuzione delle labels\n",
        "kf = StratifiedKFold(5)\n",
        "mean_accuracies = []\n",
        "for config in ParameterGrid(params):\n",
        "  clf_accuracies = []\n",
        "  counts = []\n",
        "  for train_indices, valid_indices in kf.split(X_train_valid, y_train_valid):\n",
        "    X_train = X_train_valid[train_indices]\n",
        "    y_train = y_train_valid[train_indices]\n",
        "    X_valid = X_train_valid[valid_indices]\n",
        "    y_valid = y_train_valid[valid_indices]\n",
        "\n",
        "    counts.append(len(train_indices)) #fissati i parametri farò media della metrica pesata sulle size dei train set ottenuti nel kfold\n",
        "  \n",
        "    #QUI CI METTO I VECTORIZER, E ALTRE COSE DI PRIMA SE VOGLIO SU CUI FARE LA GRIDSEARCH\n",
        "    classifier=LinearSVC(**config, random_state=1) #fisso il random state uguale per tutti cosi\n",
        "    classifier.fit(X_train, y_train)\n",
        "    y_pred=classifier.predict(X_valid)\n",
        "\n",
        "    acc=f1_score(y_valid, y_pred, average='weighted') #weighted a seconda del supporto delle labels(sono 2 qui)\n",
        "    clf_accuracies.append(acc)\n",
        "  mean_acc = np.average(clf_accuracies, weights=counts) #media sui fold per quella config di parametri\n",
        "  print(mean_acc, str(config))\n",
        "  mean_accuracies.append(mean_acc)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9663642277031095 {'C': 0.5, 'fit_intercept': True, 'max_iter': 1000, 'tol': 0.01}\n",
            "0.9662697389352553 {'C': 0.5, 'fit_intercept': True, 'max_iter': 1000, 'tol': 0.001}\n",
            "0.9662697389352553 {'C': 0.5, 'fit_intercept': True, 'max_iter': 1000, 'tol': 0.0001}\n",
            "0.9662697389352553 {'C': 0.5, 'fit_intercept': True, 'max_iter': 1000, 'tol': 1e-06}\n",
            "0.9662697389352553 {'C': 0.5, 'fit_intercept': True, 'max_iter': 1000, 'tol': 1e-07}\n",
            "0.9663642277031095 {'C': 0.5, 'fit_intercept': True, 'max_iter': 3000, 'tol': 0.01}\n",
            "0.9662697389352553 {'C': 0.5, 'fit_intercept': True, 'max_iter': 3000, 'tol': 0.001}\n",
            "0.9662697389352553 {'C': 0.5, 'fit_intercept': True, 'max_iter': 3000, 'tol': 0.0001}\n",
            "0.9662697389352553 {'C': 0.5, 'fit_intercept': True, 'max_iter': 3000, 'tol': 1e-06}\n",
            "0.9662697389352553 {'C': 0.5, 'fit_intercept': True, 'max_iter': 3000, 'tol': 1e-07}\n",
            "0.9663629341033072 {'C': 0.5, 'fit_intercept': False, 'max_iter': 1000, 'tol': 0.01}\n",
            "0.9663629341033072 {'C': 0.5, 'fit_intercept': False, 'max_iter': 1000, 'tol': 0.001}\n",
            "0.9663629341033072 {'C': 0.5, 'fit_intercept': False, 'max_iter': 1000, 'tol': 0.0001}\n",
            "0.9663629341033072 {'C': 0.5, 'fit_intercept': False, 'max_iter': 1000, 'tol': 1e-06}\n",
            "0.9663629341033072 {'C': 0.5, 'fit_intercept': False, 'max_iter': 1000, 'tol': 1e-07}\n",
            "0.9663629341033072 {'C': 0.5, 'fit_intercept': False, 'max_iter': 3000, 'tol': 0.01}\n",
            "0.9663629341033072 {'C': 0.5, 'fit_intercept': False, 'max_iter': 3000, 'tol': 0.001}\n",
            "0.9663629341033072 {'C': 0.5, 'fit_intercept': False, 'max_iter': 3000, 'tol': 0.0001}\n",
            "0.9663629341033072 {'C': 0.5, 'fit_intercept': False, 'max_iter': 3000, 'tol': 1e-06}\n",
            "0.9663629341033072 {'C': 0.5, 'fit_intercept': False, 'max_iter': 3000, 'tol': 1e-07}\n",
            "0.9665126992410917 {'C': 1.0, 'fit_intercept': True, 'max_iter': 1000, 'tol': 0.01}\n",
            "0.9666071879255151 {'C': 1.0, 'fit_intercept': True, 'max_iter': 1000, 'tol': 0.001}\n",
            "0.9666071879255151 {'C': 1.0, 'fit_intercept': True, 'max_iter': 1000, 'tol': 0.0001}\n",
            "0.9666071879255151 {'C': 1.0, 'fit_intercept': True, 'max_iter': 1000, 'tol': 1e-06}\n",
            "0.9666071879255151 {'C': 1.0, 'fit_intercept': True, 'max_iter': 1000, 'tol': 1e-07}\n",
            "0.9665126992410917 {'C': 1.0, 'fit_intercept': True, 'max_iter': 3000, 'tol': 0.01}\n",
            "0.9666071879255151 {'C': 1.0, 'fit_intercept': True, 'max_iter': 3000, 'tol': 0.001}\n",
            "0.9666071879255151 {'C': 1.0, 'fit_intercept': True, 'max_iter': 3000, 'tol': 0.0001}\n",
            "0.9666071879255151 {'C': 1.0, 'fit_intercept': True, 'max_iter': 3000, 'tol': 1e-06}\n",
            "0.9666071879255151 {'C': 1.0, 'fit_intercept': True, 'max_iter': 3000, 'tol': 1e-07}\n",
            "0.9663278076085935 {'C': 1.0, 'fit_intercept': False, 'max_iter': 1000, 'tol': 0.01}\n",
            "0.9663278076085935 {'C': 1.0, 'fit_intercept': False, 'max_iter': 1000, 'tol': 0.001}\n",
            "0.9663278076085935 {'C': 1.0, 'fit_intercept': False, 'max_iter': 1000, 'tol': 0.0001}\n",
            "0.9663278076085935 {'C': 1.0, 'fit_intercept': False, 'max_iter': 1000, 'tol': 1e-06}\n",
            "0.9663278076085935 {'C': 1.0, 'fit_intercept': False, 'max_iter': 1000, 'tol': 1e-07}\n",
            "0.9663278076085935 {'C': 1.0, 'fit_intercept': False, 'max_iter': 3000, 'tol': 0.01}\n",
            "0.9663278076085935 {'C': 1.0, 'fit_intercept': False, 'max_iter': 3000, 'tol': 0.001}\n",
            "0.9663278076085935 {'C': 1.0, 'fit_intercept': False, 'max_iter': 3000, 'tol': 0.0001}\n",
            "0.9663278076085935 {'C': 1.0, 'fit_intercept': False, 'max_iter': 3000, 'tol': 1e-06}\n",
            "0.9663278076085935 {'C': 1.0, 'fit_intercept': False, 'max_iter': 3000, 'tol': 1e-07}\n",
            "0.9655908396002113 {'C': 2.0, 'fit_intercept': True, 'max_iter': 1000, 'tol': 0.01}\n",
            "0.9655466629560499 {'C': 2.0, 'fit_intercept': True, 'max_iter': 1000, 'tol': 0.001}\n",
            "0.9655924191889083 {'C': 2.0, 'fit_intercept': True, 'max_iter': 1000, 'tol': 0.0001}\n",
            "0.9655924191889083 {'C': 2.0, 'fit_intercept': True, 'max_iter': 1000, 'tol': 1e-06}\n",
            "0.9655924191889083 {'C': 2.0, 'fit_intercept': True, 'max_iter': 1000, 'tol': 1e-07}\n",
            "0.9655908396002113 {'C': 2.0, 'fit_intercept': True, 'max_iter': 3000, 'tol': 0.01}\n",
            "0.9655466629560499 {'C': 2.0, 'fit_intercept': True, 'max_iter': 3000, 'tol': 0.001}\n",
            "0.9655924191889083 {'C': 2.0, 'fit_intercept': True, 'max_iter': 3000, 'tol': 0.0001}\n",
            "0.9655924191889083 {'C': 2.0, 'fit_intercept': True, 'max_iter': 3000, 'tol': 1e-06}\n",
            "0.9655924191889083 {'C': 2.0, 'fit_intercept': True, 'max_iter': 3000, 'tol': 1e-07}\n",
            "0.9655937241874498 {'C': 2.0, 'fit_intercept': False, 'max_iter': 1000, 'tol': 0.01}\n",
            "0.9655937241874498 {'C': 2.0, 'fit_intercept': False, 'max_iter': 1000, 'tol': 0.001}\n",
            "0.9655937241874498 {'C': 2.0, 'fit_intercept': False, 'max_iter': 1000, 'tol': 0.0001}\n",
            "0.9655937241874498 {'C': 2.0, 'fit_intercept': False, 'max_iter': 1000, 'tol': 1e-06}\n",
            "0.9655937241874498 {'C': 2.0, 'fit_intercept': False, 'max_iter': 1000, 'tol': 1e-07}\n",
            "0.9655937241874498 {'C': 2.0, 'fit_intercept': False, 'max_iter': 3000, 'tol': 0.01}\n",
            "0.9655937241874498 {'C': 2.0, 'fit_intercept': False, 'max_iter': 3000, 'tol': 0.001}\n",
            "0.9655937241874498 {'C': 2.0, 'fit_intercept': False, 'max_iter': 3000, 'tol': 0.0001}\n",
            "0.9655937241874498 {'C': 2.0, 'fit_intercept': False, 'max_iter': 3000, 'tol': 1e-06}\n",
            "0.9655937241874498 {'C': 2.0, 'fit_intercept': False, 'max_iter': 3000, 'tol': 1e-07}\n",
            "0.9650842709164948 {'C': 3.0, 'fit_intercept': True, 'max_iter': 1000, 'tol': 0.01}\n",
            "0.9650842709164948 {'C': 3.0, 'fit_intercept': True, 'max_iter': 1000, 'tol': 0.001}\n",
            "0.9650842709164948 {'C': 3.0, 'fit_intercept': True, 'max_iter': 1000, 'tol': 0.0001}\n",
            "0.9650842709164948 {'C': 3.0, 'fit_intercept': True, 'max_iter': 1000, 'tol': 1e-06}\n",
            "0.9650842709164948 {'C': 3.0, 'fit_intercept': True, 'max_iter': 1000, 'tol': 1e-07}\n",
            "0.9650842709164948 {'C': 3.0, 'fit_intercept': True, 'max_iter': 3000, 'tol': 0.01}\n",
            "0.9650842709164948 {'C': 3.0, 'fit_intercept': True, 'max_iter': 3000, 'tol': 0.001}\n",
            "0.9650842709164948 {'C': 3.0, 'fit_intercept': True, 'max_iter': 3000, 'tol': 0.0001}\n",
            "0.9650842709164948 {'C': 3.0, 'fit_intercept': True, 'max_iter': 3000, 'tol': 1e-06}\n",
            "0.9650842709164948 {'C': 3.0, 'fit_intercept': True, 'max_iter': 3000, 'tol': 1e-07}\n",
            "0.9650381436220806 {'C': 3.0, 'fit_intercept': False, 'max_iter': 1000, 'tol': 0.01}\n",
            "0.9650381436220806 {'C': 3.0, 'fit_intercept': False, 'max_iter': 1000, 'tol': 0.001}\n",
            "0.9650381436220806 {'C': 3.0, 'fit_intercept': False, 'max_iter': 1000, 'tol': 0.0001}\n",
            "0.9650381436220806 {'C': 3.0, 'fit_intercept': False, 'max_iter': 1000, 'tol': 1e-06}\n",
            "0.9650381436220806 {'C': 3.0, 'fit_intercept': False, 'max_iter': 1000, 'tol': 1e-07}\n",
            "0.9650381436220806 {'C': 3.0, 'fit_intercept': False, 'max_iter': 3000, 'tol': 0.01}\n",
            "0.9650381436220806 {'C': 3.0, 'fit_intercept': False, 'max_iter': 3000, 'tol': 0.001}\n",
            "0.9650381436220806 {'C': 3.0, 'fit_intercept': False, 'max_iter': 3000, 'tol': 0.0001}\n",
            "0.9650381436220806 {'C': 3.0, 'fit_intercept': False, 'max_iter': 3000, 'tol': 1e-06}\n",
            "0.9650381436220806 {'C': 3.0, 'fit_intercept': False, 'max_iter': 3000, 'tol': 1e-07}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eW3-Ek7gQLNj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "65752dfd-7e92-4198-db35-1549316819a4"
      },
      "source": [
        "#alla fine della cross validation ho scelto LA BEST CONFIGURATION\n",
        "#quindi traino sull' X_train_valid e predico X_test (che è labellato ma era rimasto comunque fuori)\n",
        "#se ottengo un risultato gradevole posso TRAINARE su tutto il labelled e lo userò per trovare le label unknown\n",
        "best_config = list(ParameterGrid(params))[np.argmax(mean_accuracies)]\n",
        "classifier=LinearSVC(**best_config)\n",
        "classifier.fit(X_train_valid, y_train_valid)\n",
        "print(f1_score(y_test, classifier.predict(X_test), average='weighted'))"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9648351803739017\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s24Ozb4QQ2Bd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#traino su tutto il labelled e predico le unknown\n",
        "classifier=LinearSVC(**best_config)\n",
        "classifier.fit(sparse_matrix[:number_of_labelled], total_df['class'][:number_of_labelled])\n",
        "all_prediction_linear = classifier.predict(sparse_matrix)\n",
        "def dump_to_file(file, labels):\n",
        "    with open(file, 'w', encoding='utf-8', newline='') as writeFile:\n",
        "        writer = csv.writer(writeFile)\n",
        "        writer.writerow(['Id', 'Predicted'])\n",
        "        writer.writerows(enumerate(labels))\n",
        "\n",
        "dump_to_file('res.csv', all_prediction_linear[number_of_labelled:])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-S-_Y1RadXdf",
        "colab_type": "text"
      },
      "source": [
        "ALTRI TENTATIVI EFFETTUATI MA NON SCELTI NELLA VERSIONE FINALE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x1172IguGE5K",
        "colab_type": "text"
      },
      "source": [
        "SUPPORT VECTOR MACHINE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1VvsJXnodbpA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#features_df=pd.DataFrame.sparse.from_spmatrix(sparse_matrix)\n",
        "#cosi applico la SVM sulle feature che ho ottenuto dal vectorizer\n",
        "\n",
        "# Perform classification with SVM, kernel=linear \n",
        "#classifier_linear = svm.SVC(kernel='linear')\n",
        "\n",
        "#classifier_linear.fit(sparse_matrix[:number_of_labelled], total_df['class'][:number_of_labelled])\n",
        "#traino solo sulle label\n",
        "\n",
        "#PREDIZIONE\n",
        "#all_prediction_linear = classifier_linear.predict(sparse_matrix)\n",
        "#dump_to_file('res.csv', all_prediction_linear[number_of_labelled:])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "POv4nDY7eRiX",
        "colab_type": "text"
      },
      "source": [
        "RULE BASED"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cDHucoIQeRPy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#def polarity(word):\n",
        "#      return (np.array(total_df['class'][:number_of_labelled]  == 'pos') & np.array(total_df['text_tokenized_lemmed'][:number_of_labelled].apply(lambda text: word in text))).sum() / np.array(total_df['text_tokenized_lemmed'][:number_of_labelled] .apply(lambda text: word in text)).sum() * 100 \n",
        "\n",
        "\n",
        "#commonwords = [x[0] for x in Counter(words).most_common()[:5000] ]\n",
        "\n",
        "#polarity_words = {x: polarity(x) for x in commonwords }\n",
        "\n",
        "#def polarity_review(review):\n",
        "#    return np.array(list(map(lambda x: polarity_words.get(x, 50), review))).mean()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MzV-25dSezEm",
        "colab_type": "text"
      },
      "source": [
        "NAIVE BAYESIAN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0WNBDNMe0Hr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#apriori_pos = (total_df['class'] == 'pos').sum() / number_of_labelled\n",
        "#apriori_neg = (total_df['class'] == 'neg').sum() / number_of_labelled\n",
        "\n",
        "#def feature_prob_pos(review):\n",
        "#    return np.array(list(map(lambda x: polarity_words.get(x, 50) / 100, review))).prod()* apriori_pos \n",
        "\n",
        "#def feature_prob_neg(review):\n",
        "#    return np.array(list(map(lambda x: 1-polarity_words.get(x, 50) / 100, review))).prod()* apriori_neg "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EeSL6Qwx4_yh",
        "colab_type": "text"
      },
      "source": [
        "RANDOM FOREST"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A-Ou6BIU5Al0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#params = {\n",
        "#\"max_depth\": [None, 30, 40, 50],\n",
        "#\"min_impurity_decrease\": [0, 1e-1, 1e-2, 1e-4],\n",
        "#\"n_estimators\": [50,200,500]\n",
        "#}\n",
        "#X_train_valid, X_test, y_train_valid, y_test=train_test_split(sparse_matrix[:number_of_labelled], np.array(y_true),                                                              stratify=np.array(y_true))\n",
        "#kf = StratifiedKFold(5)\n",
        "#accuracies = []\n",
        "#for config in ParameterGrid(params):\n",
        "#  clf_accuracies = []\n",
        "#  counts = []\n",
        "#  for train_indices, valid_indices in kf.split(X_train_valid, y_train_valid):\n",
        "#    X_train = X_train_valid[train_indices]\n",
        "#    y_train = y_train_valid[train_indices]\n",
        "#    X_valid = X_train_valid[valid_indices]\n",
        "#    y_valid = y_train_valid[valid_indices]\n",
        "#    counts.append(len(train_indices))\n",
        "#    clf = RandomForestClassifier(**config, random_state=1)\n",
        "#    clf.fit(X_train, y_train)\n",
        "#    y_pred=clf.predict(X_valid)\n",
        "#    acc = f1_score(y_valid, y_pred, average='weighted')\n",
        "#    clf_accuracies.append(acc)\n",
        "#  mean_acc = np.average(clf_accuracies, weights=counts)\n",
        "#  accuracies.append(mean_acc)\n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}