tweets_analysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ArTeDS/Sentiment-Analysis-on-Streaming-Covid_19-Tweets/blob/main/tweets_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "992ab113-27a5-4607-a453-4ea26802380c",
      "metadata": {
        "id": "992ab113-27a5-4607-a453-4ea26802380c"
      },
      "source": [
        "## Load and Preprocess Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "04229442-2043-426e-ad24-11d00501087a",
      "metadata": {
        "id": "04229442-2043-426e-ad24-11d00501087a",
        "outputId": "433e542a-f03b-42fd-bc52-4d43bbf9ca96"
      },
      "outputs": [
        {
          "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>word</th>\n",
              "      <th>polarity</th>\n",
              "      <th>subjectivity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td></td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>One of the weird things over the last few ye...</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>0.30333333333333334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Petition Do not make COVID vaccination a requi...</td>\n",
              "      <td>0.03333333333333333</td>\n",
              "      <td>0.06666666666666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>If you go to the South they have been over C...</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>If you go to the South they have been over C...</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43161</th>\n",
              "      <td>Almost 2000 people a week are dying with cov...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43162</th>\n",
              "      <td>Spain has some of the most beautiful horse's â...</td>\n",
              "      <td>0.675</td>\n",
              "      <td>0.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43163</th>\n",
              "      <td>- Claim pandemic is over</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43164</th>\n",
              "      <td>âš½ï¸� In game fatigue</td>\n",
              "      <td>-0.4</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43165</th>\n",
              "      <td>COVID related (19)</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>43166 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    word             polarity  \\\n",
              "0                                                                         0.0   \n",
              "1        One of the weird things over the last few ye...                -0.09   \n",
              "2      Petition Do not make COVID vaccination a requi...  0.03333333333333333   \n",
              "3        If you go to the South they have been over C...                 0.35   \n",
              "4        If you go to the South they have been over C...                 0.35   \n",
              "...                                                  ...                  ...   \n",
              "43161    Almost 2000 people a week are dying with cov...                  0.0   \n",
              "43162  Spain has some of the most beautiful horse's â...                0.675   \n",
              "43163                           - Claim pandemic is over                  0.0   \n",
              "43164                            âš½ï¸� In game fatigue                  -0.4   \n",
              "43165                                 COVID related (19)                  0.0   \n",
              "\n",
              "              subjectivity  \n",
              "0                      0.0  \n",
              "1      0.30333333333333334  \n",
              "2      0.06666666666666667  \n",
              "3                     0.55  \n",
              "4                     0.55  \n",
              "...                    ...  \n",
              "43161                  0.0  \n",
              "43162                 0.75  \n",
              "43163                  0.0  \n",
              "43164                  0.4  \n",
              "43165                  0.4  \n",
              "\n",
              "[43166 rows x 3 columns]"
            ]
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# laod the parquet files into a dataframe\n",
        "#! pip install pyarrow\n",
        "import pandas as pd\n",
        "parguet_file = r'C:\\Data Science\\MSc Data Science\\Big Data Management and Processing\\Project\\Twitter_Streaming_Sentiment_Analysis\\parquet'\n",
        "df=pd.read_parquet(parguet_file, engine='auto')\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "538ea61f-513a-450f-8de6-3401e8b031d5",
      "metadata": {
        "id": "538ea61f-513a-450f-8de6-3401e8b031d5",
        "outputId": "637ddc3b-5a78-4a7e-cf73-a64b47570906"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "20806\n"
          ]
        }
      ],
      "source": [
        "# drop duplicates - retweets\n",
        "df.drop_duplicates(inplace=True)\n",
        "df.reset_index(drop=True, inplace=True)\n",
        "print(len(df))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "50125e17-f686-47a2-b299-13f82cab1477",
      "metadata": {
        "id": "50125e17-f686-47a2-b299-13f82cab1477"
      },
      "outputs": [],
      "source": [
        "df['polarity'] = df['polarity'].astype(float)\n",
        "df['subjectivity'] = df['subjectivity'].astype(float)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7ef0e0e3-7c44-4115-aed1-f3bef009af49",
      "metadata": {
        "id": "7ef0e0e3-7c44-4115-aed1-f3bef009af49"
      },
      "outputs": [],
      "source": [
        "# setting labels for the polarity\n",
        "df['polarity_measure'] = ['positive' if score > 0 \n",
        "                          else 'negative' if score < 0 \n",
        "                              else 'neutral' \n",
        "                                  for score in df.polarity]\n",
        "\n",
        "# setting labels for the subjectivity\n",
        "df['subjectivity_measure'] = ['highly_objective' if score < 0.1428 \n",
        "                                    else 'objective' if score < 0.2856\n",
        "                                        else 'partially_objective' if score <0.4284\n",
        "                                            else 'neutral' if score < 0.5712\n",
        "                                                else 'partially_subjective' if score < 0.714\n",
        "                                                    else 'subjective' if score < 0.8568\n",
        "                                                        else 'highly_subjective'                                                                               \n",
        "                                                              for score in df.subjectivity]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3d2d4627-db8f-4c08-b8b5-ec1ea7ff5dfd",
      "metadata": {
        "id": "3d2d4627-db8f-4c08-b8b5-ec1ea7ff5dfd"
      },
      "source": [
        "### Text Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9d1f0070-1a91-47c5-83c6-80faddc6e8d3",
      "metadata": {
        "id": "9d1f0070-1a91-47c5-83c6-80faddc6e8d3"
      },
      "outputs": [],
      "source": [
        "# Function for text preprocessing\n",
        "\n",
        "# !pip install spacy\n",
        "# !python -m spacy download en_core_web_sm\n",
        "# nltk.download('words')\n",
        "# nltk.download('stopwords')\n",
        "\n",
        "# Function for text preprocessing\n",
        "import string\n",
        "import re\n",
        "import nltk\n",
        "from nltk.corpus import words\n",
        "from nltk.corpus import wordnet\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.snowball import SnowballStemmer\n",
        "import spacy\n",
        "\n",
        "# set puncuation and stopwords into one list\n",
        "punctuation = list(string.punctuation)\n",
        "# punctuation.remove('-')\n",
        "\n",
        "# english stopwords\n",
        "stopwords = nltk.corpus.stopwords.words('english')\n",
        "\n",
        "# load spaCY english language\n",
        "nlp = spacy.load('en_core_web_sm')\n",
        "\n",
        "# Snowball Stemmer\n",
        "stemmer = SnowballStemmer('english')\n",
        "\n",
        "\n",
        "def preprocess(df, s_column, t_column, lemmatize=False, stem=False):\n",
        "    \n",
        "    # df: DataFrame\n",
        "    # s_column: source column. The one that is to be processed\n",
        "    # t_column: target column. The one created with the preprocessed text\n",
        "    \n",
        "    # lowercase\n",
        "    \n",
        "    df[t_column] = df[s_column].str.lower()\n",
        "       \n",
        "     # remove whitespaces\n",
        "    df[t_column] = df[t_column].apply(lambda x : ( re.sub(' +', ' ', x)))\n",
        "    \n",
        "    # remove numbers and words concatenated with numbers IE h4ck3r. Removes road names such as BR-381.\n",
        "    df[t_column] = df[t_column].apply(lambda x : (re.sub(\"\\S*\\d\\S*\",\" \", x)))\n",
        "    \n",
        "    # removes emails and mentions (words with @)\n",
        "    df[t_column] = df[t_column].apply(lambda x : (re.sub(\"\\S*@\\S*\\s?\",\" \", x)))\n",
        "    \n",
        "    # remove url \n",
        "    df[t_column] = df[t_column].apply(lambda x : ( re.sub(r'http\\S+|www.\\S+', r'', x)))\n",
        "    \n",
        "    if lemmatize == True:\n",
        "        df[t_column] = df[t_column].apply(lambda x: ' '.join([w.lemma_ for w in nlp(x)]))\n",
        "    \n",
        "    # remove words with less than 3 letters \n",
        "    df[t_column] = df[t_column].apply(lambda x : (re.sub(r'\\b\\w{1,2}\\b', '', x)))\n",
        "    \n",
        "    # remove noise (e.g. numbers, symbols)\n",
        "    #df[t_column] = df[t_column].apply(lambda x : (re.sub('[^a-z\\s]', ' ',x)))\n",
        "    \n",
        "    # remove punctuation\n",
        "    df[t_column] = df[t_column].apply(lambda x: ''.join([word for word in x if word not in (punctuation)]))\n",
        "    \n",
        "    # remove words with 14 or more letters\n",
        "    df[t_column] = df[t_column].apply(lambda x : (re.sub(r'\\b\\w{14,1000}\\b', '', x)))\n",
        "    \n",
        "        \n",
        "    # stem every word\n",
        "    if stem == True:\n",
        "        # remove stopwords\n",
        "        df[t_column] = df[t_column].apply(lambda x: ([word for word in x.split() if word not in (stopwords)]))\n",
        "        \n",
        "        # stemmer\n",
        "        df[t_column] = df[t_column].apply(lambda x: ', '.join([stemmer.stem(y) for y in x])) \n",
        "    \n",
        "    else:\n",
        "        # remove stopwords\n",
        "        df[t_column] = df[t_column].apply(lambda x: ', '.join([word for word in x.split() if word not in (stopwords)]))\n",
        "        \n",
        "    return df\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0ac79252-88de-49f2-b276-c8ec4b064fd9",
      "metadata": {
        "id": "0ac79252-88de-49f2-b276-c8ec4b064fd9",
        "outputId": "e2b2a5e5-48a5-44ab-d57f-2dd1d457b1db"
      },
      "outputs": [
        {
          "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>word</th>\n",
              "      <th>polarity</th>\n",
              "      <th>subjectivity</th>\n",
              "      <th>polarity_measure</th>\n",
              "      <th>subjectivity_measure</th>\n",
              "      <th>tokens</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td></td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>neutral</td>\n",
              "      <td>highly_objective</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>One of the weird things over the last few ye...</td>\n",
              "      <td>-0.090000</td>\n",
              "      <td>0.303333</td>\n",
              "      <td>negative</td>\n",
              "      <td>partially_objective</td>\n",
              "      <td>one, weird, thing, last, year, people, consist...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Petition Do not make COVID vaccination a requi...</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>positive</td>\n",
              "      <td>highly_objective</td>\n",
              "      <td>petition, make, covid, vaccination, requiremen...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>If you go to the South they have been over C...</td>\n",
              "      <td>0.350000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>positive</td>\n",
              "      <td>neutral</td>\n",
              "      <td>south, covid, like, full, year</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>The bullshitandspin machine is off the scale...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>neutral</td>\n",
              "      <td>highly_objective</td>\n",
              "      <td>machine, scale</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20801</th>\n",
              "      <td>Boris said 'Let the bodies pile high'! - and t...</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.540000</td>\n",
              "      <td>positive</td>\n",
              "      <td>neutral</td>\n",
              "      <td>boris, say, let, body, pile, high</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20802</th>\n",
              "      <td>Almost 2000 people a week are dying with cov...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>neutral</td>\n",
              "      <td>highly_objective</td>\n",
              "      <td>almost, people, week, die, covid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20803</th>\n",
              "      <td>Spain has some of the most beautiful horse's â...</td>\n",
              "      <td>0.675000</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>positive</td>\n",
              "      <td>subjective</td>\n",
              "      <td>spain, beautiful, horse, �, ¤¸, �</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20804</th>\n",
              "      <td>âš½ï¸� In game fatigue</td>\n",
              "      <td>-0.400000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>negative</td>\n",
              "      <td>partially_objective</td>\n",
              "      <td>âš½ï¸, �, game, fatigue</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20805</th>\n",
              "      <td>COVID related (19)</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>neutral</td>\n",
              "      <td>partially_objective</td>\n",
              "      <td>covid, related</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20806 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    word  polarity  \\\n",
              "0                                                         0.000000   \n",
              "1        One of the weird things over the last few ye... -0.090000   \n",
              "2      Petition Do not make COVID vaccination a requi...  0.033333   \n",
              "3        If you go to the South they have been over C...  0.350000   \n",
              "4        The bullshitandspin machine is off the scale...  0.000000   \n",
              "...                                                  ...       ...   \n",
              "20801  Boris said 'Let the bodies pile high'! - and t...  0.250000   \n",
              "20802    Almost 2000 people a week are dying with cov...  0.000000   \n",
              "20803  Spain has some of the most beautiful horse's â...  0.675000   \n",
              "20804                            âš½ï¸� In game fatigue  -0.400000   \n",
              "20805                                 COVID related (19)  0.000000   \n",
              "\n",
              "       subjectivity polarity_measure subjectivity_measure  \\\n",
              "0          0.000000          neutral     highly_objective   \n",
              "1          0.303333         negative  partially_objective   \n",
              "2          0.066667         positive     highly_objective   \n",
              "3          0.550000         positive              neutral   \n",
              "4          0.000000          neutral     highly_objective   \n",
              "...             ...              ...                  ...   \n",
              "20801      0.540000         positive              neutral   \n",
              "20802      0.000000          neutral     highly_objective   \n",
              "20803      0.750000         positive           subjective   \n",
              "20804      0.400000         negative  partially_objective   \n",
              "20805      0.400000          neutral  partially_objective   \n",
              "\n",
              "                                                  tokens  \n",
              "0                                                         \n",
              "1      one, weird, thing, last, year, people, consist...  \n",
              "2      petition, make, covid, vaccination, requiremen...  \n",
              "3                         south, covid, like, full, year  \n",
              "4                                         machine, scale  \n",
              "...                                                  ...  \n",
              "20801                  boris, say, let, body, pile, high  \n",
              "20802                   almost, people, week, die, covid  \n",
              "20803                  spain, beautiful, horse, �, ¤¸, �  \n",
              "20804                            âš½ï¸, �, game, fatigue  \n",
              "20805                                     covid, related  \n",
              "\n",
              "[20806 rows x 6 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "preprocess(df, 'word', 'tokens', lemmatize=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2e8dd9b5-aa00-4249-ab29-dfc12bc3417a",
      "metadata": {
        "id": "2e8dd9b5-aa00-4249-ab29-dfc12bc3417a"
      },
      "source": [
        "## Predictions for tweets containing real and fake news regarding Covid-19"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c7f12d34-d0f0-4dd1-99f6-52a792aba435",
      "metadata": {
        "id": "c7f12d34-d0f0-4dd1-99f6-52a792aba435"
      },
      "outputs": [],
      "source": [
        "# Load the pretrained model for identifying which of the tweets contain real news and fake news about covid19\n",
        "import joblib\n",
        "MNB_Covid_Tweets_Real_Fake = joblib.load('MNB_Covid_Tweets_Real_Fake.pkl')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "405304d7-49ab-45ba-ae50-cb8b8696582b",
      "metadata": {
        "id": "405304d7-49ab-45ba-ae50-cb8b8696582b",
        "outputId": "8a4ab835-befe-492a-ba6a-5883b8057b3b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "20806\n"
          ]
        }
      ],
      "source": [
        "# Use the loaded model to make predictions\n",
        "y_pred = MNB_Covid_Tweets_Real_Fake.predict(df.tokens)\n",
        "print(len(y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ddc9148c-c9fc-4fb3-a29f-7d2fb09f91f1",
      "metadata": {
        "id": "ddc9148c-c9fc-4fb3-a29f-7d2fb09f91f1",
        "outputId": "033e4749-60aa-4ddf-b6b5-d03fdd95f018"
      },
      "outputs": [
        {
          "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>word</th>\n",
              "      <th>polarity</th>\n",
              "      <th>subjectivity</th>\n",
              "      <th>polarity_measure</th>\n",
              "      <th>subjectivity_measure</th>\n",
              "      <th>tokens</th>\n",
              "      <th>real_fake_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td></td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>neutral</td>\n",
              "      <td>highly_objective</td>\n",
              "      <td></td>\n",
              "      <td>fake</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>One of the weird things over the last few ye...</td>\n",
              "      <td>-0.090000</td>\n",
              "      <td>0.303333</td>\n",
              "      <td>negative</td>\n",
              "      <td>partially_objective</td>\n",
              "      <td>one, weird, thing, last, year, people, consist...</td>\n",
              "      <td>fake</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Petition Do not make COVID vaccination a requi...</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>positive</td>\n",
              "      <td>highly_objective</td>\n",
              "      <td>petition, make, covid, vaccination, requiremen...</td>\n",
              "      <td>real</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>If you go to the South they have been over C...</td>\n",
              "      <td>0.350000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>positive</td>\n",
              "      <td>neutral</td>\n",
              "      <td>south, covid, like, full, year</td>\n",
              "      <td>real</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>The bullshitandspin machine is off the scale...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>neutral</td>\n",
              "      <td>highly_objective</td>\n",
              "      <td>machine, scale</td>\n",
              "      <td>real</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                word  polarity  subjectivity  \\\n",
              "0                                                     0.000000      0.000000   \n",
              "1    One of the weird things over the last few ye... -0.090000      0.303333   \n",
              "2  Petition Do not make COVID vaccination a requi...  0.033333      0.066667   \n",
              "3    If you go to the South they have been over C...  0.350000      0.550000   \n",
              "4    The bullshitandspin machine is off the scale...  0.000000      0.000000   \n",
              "\n",
              "  polarity_measure subjectivity_measure  \\\n",
              "0          neutral     highly_objective   \n",
              "1         negative  partially_objective   \n",
              "2         positive     highly_objective   \n",
              "3         positive              neutral   \n",
              "4          neutral     highly_objective   \n",
              "\n",
              "                                              tokens real_fake_label  \n",
              "0                                                               fake  \n",
              "1  one, weird, thing, last, year, people, consist...            fake  \n",
              "2  petition, make, covid, vaccination, requiremen...            real  \n",
              "3                     south, covid, like, full, year            real  \n",
              "4                                     machine, scale            real  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# add a column witht the predictions on the dataframe\n",
        "df['real_fake'] = y_pred\n",
        "\n",
        "# create column with string labels for real and fake news\n",
        "label = {0: 'fake',\n",
        "         1: 'real'}\n",
        "\n",
        "df['real_fake_label'] = df['real_fake'].map(label)\n",
        "\n",
        "# drop boolean labels\n",
        "df.drop(columns='real_fake', inplace=True)\n",
        "df.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ca4cc912-fd58-4cae-98fb-308f3b23cbc1",
      "metadata": {
        "id": "ca4cc912-fd58-4cae-98fb-308f3b23cbc1"
      },
      "source": [
        "## Data Analysis\n",
        "\n",
        "### Real and Fake News"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "88b7f733-43eb-49bb-b004-d8ab02c9992b",
      "metadata": {
        "id": "88b7f733-43eb-49bb-b004-d8ab02c9992b",
        "outputId": "61beee17-b892-4ce5-bed6-4f60dc5c2415"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "fake    13839\n",
              "real     6967\n",
              "Name: real_fake_label, dtype: int64"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.real_fake_label.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0ba85df0-c1fd-4df3-9a36-f9e6e46cacd7",
      "metadata": {
        "id": "0ba85df0-c1fd-4df3-9a36-f9e6e46cacd7",
        "outputId": "8672c339-b3d4-468c-850e-d9147734c132"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot labels count\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set(style=\"whitegrid\")\n",
        "fig, axes = plt.subplots(ncols=2, \n",
        "                         nrows=1, \n",
        "                         figsize=(6, 4), \n",
        "                         dpi=100)\n",
        "fig.subplots_adjust(top=0.9, wspace=0.25)\n",
        "\n",
        "g = sns.countplot(x=df['real_fake_label'], ax=axes[0])  \n",
        "             #order=['real', 'fake'])\n",
        "\n",
        "labels=['fake', 'real']\n",
        "\n",
        "# add annotations to bar chart\n",
        "for bar in g.patches:\n",
        "    g.annotate(format(bar.get_height(), '.0f'),  # the number before f represents the decimals \n",
        "                   (bar.get_x() + bar.get_width() / 2,  \n",
        "                    bar.get_height()), ha='center', va='center', \n",
        "                   size=10, xytext=(0, -12), \n",
        "                   textcoords='offset points')\n",
        "\n",
        "axes[1].pie(df['real_fake_label'].value_counts(),\n",
        "            labels=labels,\n",
        "            autopct='%1.0f%%',\n",
        "            shadow=True,\n",
        "            startangle=90,\n",
        "            explode = (0.05, 0.05))\n",
        "\n",
        "plt.tick_params(direction='out', axis='x', labelsize=20, length=6, width=2, grid_alpha=0.5)\n",
        "plt.xlabel('count',size=0) \n",
        "plt.ylabel('', size=0) \n",
        "\n",
        "sns.color_palette(\"pastel\")\n",
        " \n",
        "fig.suptitle('Count of Tweets with Fake and Real News about Covid-19', fontsize=12)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5d30aecc-7b46-4106-aa41-98b68ab5d339",
      "metadata": {
        "tags": [],
        "id": "5d30aecc-7b46-4106-aa41-98b68ab5d339"
      },
      "source": [
        "### Polarity and Subjectivity\n",
        "\n",
        "The polarity score is a float within the range **[-1.0, 1.0]**.  \n",
        "The subjectivity is a float within the range **[0.0, 1.0]** where 0.0 is very objective and 1.0 is very subjective.  \n",
        "Let’s use this now to get the sentiment polarity and labels for each news article and aggregate the summary statistics per news category.  \n",
        "\n",
        "<br>\n",
        "\n",
        "Subjectivity quantifies the amount of personal opinion and factual information contained in the text. The higher subjectivity means that the text contains personal opinion rather than factual information. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "97b4fc1c-7397-4969-9070-2e4f3edd48f0",
      "metadata": {
        "id": "97b4fc1c-7397-4969-9070-2e4f3edd48f0",
        "outputId": "ff2e295b-34af-43c2-ae71-de5d7bf500a9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "subjectivity_measure\n",
              "highly_objective        8922\n",
              "highly_subjective       1474\n",
              "neutral                 3411\n",
              "objective               1230\n",
              "partially_objective     2560\n",
              "partially_subjective    2009\n",
              "subjective              1200\n",
              "dtype: int64"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.groupby('subjectivity_measure').size()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "73ec28b1-c9e0-4291-b230-0c75708ac76a",
      "metadata": {
        "id": "73ec28b1-c9e0-4291-b230-0c75708ac76a",
        "outputId": "42de7e27-e6c7-4485-8db9-486df178923f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "highly_objective        8922\n",
              "neutral                 3411\n",
              "partially_objective     2560\n",
              "partially_subjective    2009\n",
              "highly_subjective       1474\n",
              "objective               1230\n",
              "subjective              1200\n",
              "Name: subjectivity_measure, dtype: int64"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.subjectivity_measure.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0490581f-a016-4996-9550-2df1e393d447",
      "metadata": {
        "id": "0490581f-a016-4996-9550-2df1e393d447",
        "outputId": "ea4bd80c-9c9b-4ef4-fa3f-c4fa20ef1e13"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, '')"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "fig = plt.figure(figsize = (9,6))\n",
        "# Ploting the Subjectivity Measure\n",
        "g = sns.barplot(x=df.groupby('subjectivity_measure').size().sort_values(ascending=False).values, \n",
        "                    y=df.groupby('subjectivity_measure').size().sort_values(ascending=False).index,\n",
        "                    )\n",
        "# annotations\n",
        "for bar in g.patches:\n",
        "    g.annotate(format(bar.get_width(), '.0f'), xy=(bar.get_width(), bar.get_y()+bar.get_height()/2),\n",
        "            xytext=(-30, 0), size=12, textcoords='offset points', ha=\"left\", va=\"center\")\n",
        "\n",
        "plt.title('Subjectivity Measure')\n",
        "plt.xlabel('Number of Tweets')\n",
        "plt.ylabel('')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d1c59588-7cef-4ac5-8daf-df112829fac2",
      "metadata": {
        "id": "d1c59588-7cef-4ac5-8daf-df112829fac2",
        "outputId": "3dd8c2a4-ea68-4459-d4a3-b0df5d2aa071"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, '')"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ploting the Polarity measure\n",
        "fig = plt.figure(figsize = (6,4))\n",
        "g = sns.barplot(x=df.groupby('polarity_measure').size().sort_values(ascending=False).values, \n",
        "                    y=df.groupby('polarity_measure').size().sort_values(ascending=False).index\n",
        "                    )\n",
        "# annotations\n",
        "for bar in g.patches:\n",
        "    g.annotate(format(bar.get_width(), '.0f'), xy=(bar.get_width(), bar.get_y()+bar.get_height()/2),\n",
        "            xytext=(-30, 0), size=12, textcoords='offset points', ha=\"left\", va=\"center\")\n",
        "    \n",
        "plt.title('Polarity Measure')\n",
        "plt.xlabel('Number of Tweets')\n",
        "plt.ylabel('')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "274aa8e9-544f-4a21-be9e-b9b9e39a8fbf",
      "metadata": {
        "id": "274aa8e9-544f-4a21-be9e-b9b9e39a8fbf"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "name": "tweets_analysis.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}