Untitled4.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled4.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNfEAwOUPei2JEGdDsUQvdt"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "#https://analyticsindiamag.com/hands-on-guide-to-download-analyze-and-visualize-twitter-data/\n",
        "#Importing Libraries\n",
        "import tweepy\n",
        "from textblob import TextBlob\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import re\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "from wordcloud import WordCloud\n",
        "import json\n",
        "from collections import Counter"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxVmys6F3Vol",
        "outputId": "5a091913-08ba-4186-d680-f2c6314a5688"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# API credentials here\n",
        "consumer_key= 'insert'\n",
        "consumer_secret= 'insert'\n",
        "access_token= 'insert'\n",
        "access_token_secret= 'insert'\n",
        "\n",
        "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
        "auth.set_access_token(access_token, access_token_secret)\n",
        "api = tweepy.API(auth,wait_on_rate_limit=True,wait_on_rate_limit_notify=True)"
      ],
      "metadata": {
        "id": "QBBgYcQs_k_p"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Defining Search keyword and number of tweets and searching tweets\n",
        "query = 'little miss'\n",
        "max_tweets = 2000\n",
        "searched_tweets = [status for status in tweepy.Cursor(api.search, q=query).items(max_tweets)]\n"
      ],
      "metadata": {
        "id": "22cqjnkn_sdp"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "If this works attempt again without max tweets so can plot time series data"
      ],
      "metadata": {
        "id": "jcEEYHDuAHTc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating Dataframe of Tweets\n",
        "#Cleaning searched tweets and converting into Dataframe\n",
        "my_list_of_dicts = []\n",
        "for each_json_tweet in searched_tweets:\n",
        "    my_list_of_dicts.append(each_json_tweet._json)\n",
        "\n",
        "    \n",
        "with open('tweet_json_Data.txt', 'w') as file:\n",
        "        file.write(json.dumps(my_list_of_dicts, indent=4))\n",
        "\n",
        "        \n",
        "my_demo_list = []\n",
        "with open('tweet_json_Data.txt', encoding='utf-8') as json_file:  \n",
        "    all_data = json.load(json_file)\n",
        "    for each_dictionary in all_data:\n",
        "        tweet_id = each_dictionary['id']\n",
        "        text = each_dictionary['text']\n",
        "        favorite_count = each_dictionary['favorite_count']\n",
        "        retweet_count = each_dictionary['retweet_count']\n",
        "        created_at = each_dictionary['created_at']\n",
        "        my_demo_list.append({'tweet_id': str(tweet_id),\n",
        "                             'text': str(text),\n",
        "                             'favorite_count': int(favorite_count),\n",
        "                             'retweet_count': int(retweet_count),\n",
        "                             'created_at': created_at,\n",
        "                            })\n",
        "        \n",
        "        tweet_dataset = pd.DataFrame(my_demo_list, columns = \n",
        "                                  ['tweet_id', 'text', \n",
        "                                   'favorite_count', 'retweet_count', \n",
        "                                   'created_at'])\n",
        "\n",
        " #Writing tweet dataset ti csv file for future reference\n",
        "tweet_dataset.to_csv('tweet_data.csv')"
      ],
      "metadata": {
        "id": "OPCHP9O0AMSz"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Sentiment Analysis Report\n",
        "#Finding sentiment analysis (+ve, -ve and neutral)\n",
        "pos = 0\n",
        "neg = 0\n",
        "neu = 0\n",
        "for tweet in searched_tweets:\n",
        "    analysis = TextBlob(tweet.text)\n",
        "    if analysis.sentiment[0]>0:\n",
        "       pos = pos +1\n",
        "    elif analysis.sentiment[0]<0:\n",
        "       neg = neg + 1\n",
        "    else:\n",
        "       neu = neu + 1\n",
        "print(\"Total Positive = \", pos)\n",
        "print(\"Total Negative = \", neg)\n",
        "print(\"Total Neutral = \", neu)\n",
        "\n",
        "#Plotting sentiments\n",
        "labels = 'Positive', 'Negative', 'Neutral'\n",
        "sizes = [257, 223, 520]\n",
        "colors = ['pink', 'purple', 'yellow']\n",
        "explode = (0.1, 0, 0)  # explode 1st slice\n",
        "plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=140)\n",
        "plt.axis('equal')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "RXgZlms1C8c-",
        "outputId": "37bb9359-d01a-48cd-9d09-5cce112f2860"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Positive =  171\n",
            "Total Negative =  1823\n",
            "Total Neutral =  6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tweet_dataset.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ot5zqCzDAVUw",
        "outputId": "9e947f49-eeb4-4df2-f3dd-110925f6449b"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              tweet_id                                               text  \\\n",
              "0  1552369455723945984  RT @therealsaniyaaa: little miss don’t believe...   \n",
              "1  1552369453844664321  RT @LittleMissNotes: little miss can’t fall as...   \n",
              "2  1552369453798662144  RT @ScoutRipley: I will miss her the most (lit...   \n",
              "3  1552369453475581952  To me the little Miss meme shows that the seco...   \n",
              "4  1552369452943134722     RT @RaeBeaute: Little miss I want to be tiktok   \n",
              "\n",
              "   favorite_count  retweet_count                      created_at  \n",
              "0               0           3756  Wed Jul 27 19:04:54 +0000 2022  \n",
              "1               0           1149  Wed Jul 27 19:04:54 +0000 2022  \n",
              "2               0              3  Wed Jul 27 19:04:54 +0000 2022  \n",
              "3               0              0  Wed Jul 27 19:04:53 +0000 2022  \n",
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              "      <th>0</th>\n",
              "      <td>1552369455723945984</td>\n",
              "      <td>RT @therealsaniyaaa: little miss don’t believe...</td>\n",
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              "      <td>3756</td>\n",
              "      <td>Wed Jul 27 19:04:54 +0000 2022</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1552369453844664321</td>\n",
              "      <td>RT @LittleMissNotes: little miss can’t fall as...</td>\n",
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              "      <td>Wed Jul 27 19:04:54 +0000 2022</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1552369453798662144</td>\n",
              "      <td>RT @ScoutRipley: I will miss her the most (lit...</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Wed Jul 27 19:04:54 +0000 2022</td>\n",
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              "      <td>1552369453475581952</td>\n",
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              "      <td>Wed Jul 27 19:04:53 +0000 2022</td>\n",
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              "      <th>4</th>\n",
              "      <td>1552369452943134722</td>\n",
              "      <td>RT @RaeBeaute: Little miss I want to be tiktok</td>\n",
              "      <td>0</td>\n",
              "      <td>9001</td>\n",
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              "                                                     [key], {});\n",
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          },
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Cleaning Data\n",
        "#Removing @ handle\n",
        "def remove_pattern(input_txt, pattern):\n",
        "    r = re.findall(pattern, input_txt)\n",
        "    for i in r:\n",
        "        input_txt = re.sub(i, '', input_txt)\n",
        "     \n",
        "    return input_txt \n",
        "\n",
        "tweet_dataset['text'] = np.vectorize(remove_pattern)(tweet_dataset['text'], \"@[\\w]*\")\n",
        "\n",
        "tweet_dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "84qhrPVvA0rM",
        "outputId": "4335bea4-e03b-46f0-a541-030c98e75422"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              tweet_id                                               text  \\\n",
              "0  1552369455723945984    RT : little miss don’t believe shit a nigga say   \n",
              "1  1552369453844664321                 RT : little miss can’t fall asleep   \n",
              "2  1552369453798662144  RT : I will miss her the most (little gay car)...   \n",
              "3  1552369453475581952  To me the little Miss meme shows that the seco...   \n",
              "4  1552369452943134722               RT : Little miss I want to be tiktok   \n",
              "\n",
              "   favorite_count  retweet_count                      created_at  \n",
              "0               0           3756  Wed Jul 27 19:04:54 +0000 2022  \n",
              "1               0           1149  Wed Jul 27 19:04:54 +0000 2022  \n",
              "2               0              3  Wed Jul 27 19:04:54 +0000 2022  \n",
              "3               0              0  Wed Jul 27 19:04:53 +0000 2022  \n",
              "4               0           9001  Wed Jul 27 19:04:53 +0000 2022  "
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              "      <td>RT : Little miss I want to be tiktok</td>\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-81097574-ea71-49c2-8d8b-d68ac592682d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Cleaning Tweets\n",
        "corpus = []\n",
        "for i in range(0, 1000):\n",
        "    tweet = re.sub('[^a-zA-Z0-9]', ' ', tweet_dataset['text'][i])\n",
        "    tweet = tweet.lower()\n",
        "    tweet = re.sub('rt', '', tweet)\n",
        "    tweet = re.sub('http', '', tweet)\n",
        "    tweet = re.sub('https', '', tweet)\n",
        "    tweet = tweet.split()\n",
        "    ps = PorterStemmer()\n",
        "    tweet = [ps.stem(word) for word in tweet if not word in set(stopwords.words('english'))]\n",
        "    tweet = ' '.join(tweet)\n",
        "    corpus.append(tweet)"
      ],
      "metadata": {
        "id": "6wNtmOA5A8_v"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.download(\"tweet_data.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "oZgBjcRWBwsm",
        "outputId": "9eeed4d1-e3f5-465f-b4e9-944717db4ce4"
      },
      "execution_count": 57,
      "outputs": [
        {
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            "text/plain": [
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            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
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              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
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              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
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            "application/javascript": [
              "download(\"download_049f6e66-d949-4546-a066-27e54915487a\", \"tweet_data.csv\", 255037)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}