Otros-BernoulliNB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](http://www.upm.es/sfs/Rectorado/Gabinete%20del%20Rector/Logos/UPM/EscPolitecnica/EscUpmPolit_p.gif \"UPM\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trabajo final SITC\n",
    "## Análisis de sentimientos en Twitter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Departamento de Ingeniería de Sistemas Telemáticos. Universidad Politécnica de Madrid."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Realizado por:\n",
    "- Juan Bermudo Mera\n",
    "- Margarita Bolívar Jiménez\n",
    "- Lourdes Fernández Nieto\n",
    "- Ramón Pérez Hernández\n",
    "\n",
    "© 2017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Algoritmo BernoulliNB aplicado sobre el fichero de tweets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tabla de contenidos\n",
    "\n",
    "* [Importación de datos necesarios para aplicar el algoritmo](#1.-Importación-de-datos-necesarios-para-aplicar-el-algoritmo)\n",
    "\t* [Importación de librerías](#Importación-de-librerías)\n",
    "    * [Importación de corpus y tweets](#Importación-de-corpus-y-tweets)\n",
    "    * [Tokenización y stemming](#Tokenización-y-stemming)\n",
    "* [Entrenamiento del modelo](#2.-Entrenamiento-del-modelo)\n",
    "* [Rendimiento del modelo](#3.-Rendimiento-del-modelo)\n",
    "* [Predicción de la polaridad](#4.-Predicción-de-la-polaridad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Importación de datos necesarios para aplicar el algoritmo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* ### Importación de librerías"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Lourdes/anaconda/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "/Users/Lourdes/anaconda/lib/python3.6/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# Importamos librerías. Las que no están instaladas, instalar con pip install <nombre_paquete>\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import nltk\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "from nltk.corpus import stopwords\n",
    "from string import punctuation\n",
    "from nltk.stem import SnowballStemmer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* ### Importación de corpus y tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Se importan el corpus y los tweets\n",
    "tweets_df = pd.read_excel('ficheros/Preprocesados/tweets_corpus_header.xlsx', header=0, encoding='iso8859_15')\n",
    "tweets = pd.read_excel('ficheros/TweetsConTopic/tweets_spainGeo_topic.xlsx', header=0, encoding='iso8859_15')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* ### Tokenización y stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/Lourdes/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "# Se descargan las palabras de parada en español\n",
    "nltk.download(\"stopwords\")\n",
    "spanish_stopwords = stopwords.words('spanish')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Obtenemos los signos de puntuación que se utilizan en español\n",
    "non_words = list(punctuation)\n",
    "non_words.extend(['¿', '¡'])\n",
    "non_words.extend(map(str,range(10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Se definen las funciones para realizar la tokenización y el stemming\n",
    "stemmer = SnowballStemmer('spanish')\n",
    "tknzr = TweetTokenizer(strip_handles=True, reduce_len=True)\n",
    "\n",
    "def stem_tokens(tokens, stemmer):\n",
    "    stemmed = []\n",
    "    for item in tokens:\n",
    "        stemmed.append(stemmer.stem(item))\n",
    "    return stemmed\n",
    "\n",
    "def tokenize(text):\n",
    "    # Eliminamos lo que no sean palabras\n",
    "    text = ''.join([c for c in text if c not in non_words])\n",
    "    # Tokenización\n",
    "    tokens = tknzr.tokenize(text)\n",
    "\n",
    "    # Stemming\n",
    "    try:\n",
    "        stems = stem_tokens(tokens, stemmer)\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        print(text)\n",
    "        stems = ['']\n",
    "    return stems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Entrenamiento del modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 1.0, 'binarize': 0.0, 'class_prior': None, 'fit_prior': True}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Buscamos los parámetros que podemos utilizar para entrenar el modelo\n",
    "BernoulliNB().get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=Pipeline(steps=[('vect', CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
       "        ngram_range=(1, 1), preprocessor=None,\n",
       "        stop_words=['de', 'la'... vocabulary=None)), ('cls', BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True))]),\n",
       "       fit_params={}, iid=True, n_jobs=-1,\n",
       "       param_grid={'cls__alpha': (0.001, 0.01, 0.1, 1), 'cls__fit_prior': ('True', 'False')},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creamos el pipeline para poder encadenar todos los elementos necesarios para realizar la estimación.\n",
    "# Realizamos la búsqueda mediante GridSearhCV, que es una librería de sklearn que permite realizar una\n",
    "# búsqueda de los mejores parámetros del modelo, utulizando los parámetros definidos en parameters y como\n",
    "# métrica, roc_auc (área bajo la curva ROC)\n",
    "pipeline = Pipeline([\n",
    "    ('vect',  CountVectorizer(\n",
    "                analyzer = 'word',\n",
    "                tokenizer = tokenize,\n",
    "                lowercase = True,\n",
    "                stop_words = spanish_stopwords)),\n",
    "    ('cls', BernoulliNB())])\n",
    "parameters = {\n",
    "    'cls__alpha': (0.001, 0.01, 0.1, 1),\n",
    "    'cls__fit_prior': ('True', 'False')\n",
    "}\n",
    "gs = GridSearchCV(pipeline, parameters, n_jobs=-1, scoring='roc_auc')\n",
    "gs.fit(tweets_df.content, tweets_df.polarity_bin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cls__alpha': 1, 'cls__fit_prior': 'True'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mostramos los mejores parámetros del SVC obtenidos de la búsqueda con GridSearchCV \n",
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "El mejor parámetro que encuentra GridSearchCV para BernoulliNB es de alplha = 1 y fit_prior = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ficheros/ResultadosGridSearch/grid_searchBernoulliNB.pkl']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Guardamos el resultado del GridSearchCV en un fichero de manera persistente\n",
    "from sklearn.externals import joblib\n",
    "joblib.dump(gs, 'ficheros/ResultadosGridSearch/grid_searchBernoulliNB.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "\n",
    "## 3. Rendimiento del modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85423704792147692"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mediante validación cruzada obtenemos el rendimiento del modelo\n",
    "model = BernoulliNB(alpha=1, fit_prior = 'True')\n",
    "\n",
    "vectorizer = CountVectorizer(\n",
    "    analyzer = 'word',\n",
    "    tokenizer = tokenize,\n",
    "    lowercase = True,\n",
    "    stop_words = spanish_stopwords,\n",
    "    min_df = 0,\n",
    "    max_df = 4701,\n",
    "    max_features=1000\n",
    ")\n",
    "\n",
    "tweets_df_data_features = vectorizer.fit_transform(tweets_df.content)\n",
    "tweets_df_data_features_nd = tweets_df_data_features.toarray()\n",
    "\n",
    "scores = cross_val_score(\n",
    "    model,\n",
    "    tweets_df_data_features_nd[0:len(tweets_df)],\n",
    "    y=tweets_df.polarity_bin,\n",
    "    scoring='roc_auc',\n",
    "    cv=None\n",
    "    )\n",
    "\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "El valor que se obtiene del rendimiento del modelo para la métrica de Área bajo la Curva ROC es de 0.85423704792147692"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 4. Predicción de la polaridad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Una vez que tenemos el modelo que mejor métrica nos aporta (tras realizar muchas pruebas con distintas métricas \n",
    "# y parámetros pasados al modelo), volvemos a crear un pipeline pero en este caso, pasándole los mejores parámetros\n",
    "# obtenidos del SVC para predecir qué polaridad tienen los tweets que están aún sin etiquetar\n",
    "pipeline = Pipeline([\n",
    "    ('vect', CountVectorizer(\n",
    "            analyzer = 'word',\n",
    "            tokenizer = tokenize,\n",
    "            lowercase = True,\n",
    "            stop_words = spanish_stopwords,\n",
    "            min_df = 0,\n",
    "            max_df = 26363,\n",
    "            max_features=1000\n",
    "            )),\n",
    "    ('cls', BernoulliNB(alpha=1, fit_prior = 'True'))\n",
    "])\n",
    "\n",
    "pipeline.fit(tweets_df.content, tweets_df.polarity_bin)\n",
    "tweets['polarity'] = pipeline.predict(tweets.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>content</th>\n",
       "      <th>polarity</th>\n",
       "      <th>Topic</th>\n",
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       "      <td>#Madrid #SINFILTROS #Dibujo #Draw #MadridDesco...</td>\n",
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       "      <td>I'm at Aeropuerto Adolfo Suárez Madrid-Barajas...</td>\n",
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      "text/plain": [
       "                                                 content  polarity  \\\n",
       "24625  #Madrid #SINFILTROS #Dibujo #Draw #MadridDesco...         1   \n",
       "21868  @davidlabola las autonómías tienen que recorta...         0   \n",
       "18455  How big, How ginger, How @hanabolabola Pues he...         1   \n",
       "5998   Hoy desayunamos con la taza de la peli #losAmo...         1   \n",
       "20858  Nueva Colección en Casual&amp;Chic,La Condesa,...         1   \n",
       "23186  \"Lo amigos son la familia que se escoge. @ Tor...         1   \n",
       "23384  las #nubes tienen un amplio rango de bellos gr...         1   \n",
       "13104  I'm at Parque Empresarial La Finca in Pozuelo ...         1   \n",
       "1706   Nuestro último post ️ Los 8 impresionantes ben...         1   \n",
       "17188  Gran final sanseña! Premio al mejor lector y a...         1   \n",
       "16648    #readingtime  @kinfolkmag .... @ Arroyomolinos          1   \n",
       "13816  ||| (@ Oxford University Press España in San F...         1   \n",
       "20212  Esta es una de esas sesiones que recuerdo con ...         1   \n",
       "9347   Completos extraños que fueron enredados en sus...         1   \n",
       "18239  I'm at Aeropuerto Adolfo Suárez Madrid-Barajas...         1   \n",
       "10515  Tartas ricas que sólo mamá sabe hacer ️\\n\\n#To...         1   \n",
       "10235  Las ganas que yo tengo de verle no son normale...         1   \n",
       "9226   Nuevos seguidores: 1, unfollowers: 1 (02:26) #...         1   \n",
       "9154   No renuncies solo porque las cosas se pusieron...         0   \n",
       "23597  Amamos el bacon...es uno de los consentidos de...         1   \n",
       "\n",
       "               Topic  \n",
       "24625          otros  \n",
       "21868          otros  \n",
       "18455          otros  \n",
       "5998           otros  \n",
       "20858          otros  \n",
       "23186          otros  \n",
       "23384          otros  \n",
       "13104  sitios-madrid  \n",
       "1706           otros  \n",
       "17188          otros  \n",
       "16648          otros  \n",
       "13816          otros  \n",
       "20212          otros  \n",
       "9347           otros  \n",
       "18239     aeropuerto  \n",
       "10515          otros  \n",
       "10235          otros  \n",
       "9226       followers  \n",
       "9154           otros  \n",
       "23597          otros  "
      ]
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mostramos algunos de los tweets que han sido etiquetados con la polaridad\n",
    "tweets[['content', 'polarity','Topic']].sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Guardamos los tweets en un fichero csv con su polaridad\n",
    "tweets[[ 'content', 'Latitude', 'Longitude', 'polarity','Topic']].to_csv('ficheros/TweetsConPolaridadYTopic/tweetsBernoulliNB_polarity_bin.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Guardamos los tweets en fichero excel\n",
    "tweets[['content','Latitude','Longitude','polarity','Topic']].to_excel('ficheros/TweetsConPolaridadYTopicExcel/tweetsBernoulliNB_polarity_bin.xlsx', header=True, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Licencia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El notebook está licenciado libremente bajo la licencia [Creative Commons Attribution Share-Alike](https://creativecommons.org/licenses/by/2.0/).\n",
    "\n",
    "La base del código empleado procede del trabajo de Manuel Garrido llamado [Cómo hacer Análisis de Sentimiento en español](http://pybonacci.org/2015/11/24/como-hacer-analisis-de-sentimiento-en-espanol-2/).\n",
    "\n",
    "© 2017 - Juan Bermudo Mera, Margarita Bolívar Jiménez, Lourdes Fernández Nieto, Ramón Pérez Hernández.\n",
    "\n",
    "Universidad Politécnica de Madrid."
   ]
  }
 ],
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