VADER_Sentiment.ipynb

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    "colab": {
      "name": "VADER Sentiment",
      "provenance": [],
      "authorship_tag": "ABX9TyNeHZ5RwRIPioXSdcfSgWBN",
      "include_colab_link": true
    },
    "kernelspec": {
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
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        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/koseii2122/Reddit-Vaccine-Data/blob/main/VADER_Sentiment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import nltk"
      ],
      "metadata": {
        "id": "jLW9aAWaN9Z_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('premin10.csv', engine='python', encoding='utf-8', error_bad_lines=False)"
      ],
      "metadata": {
        "id": "9e7TvL4DCB6c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[['id', 'author', 'body_pp_4', 'subreddit', 'title']]\n",
        "df = df[df['body_pp_4'].notnull()]\n",
        "df = df.loc[:, ~df.columns.str.contains('^Unnamed')]"
      ],
      "metadata": {
        "id": "t55UHjrACB3n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer"
      ],
      "metadata": {
        "id": "paCiDOfECB1T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('vader_lexicon')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3IQIt2BFO0jZ",
        "outputId": "01979e88-0983-49d4-b3a0-2f685a63e11b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "analyzer = SentimentIntensityAnalyzer()\n",
        "df['compound'] = [analyzer.polarity_scores(x)['compound'] for x in df['body_pp_4']]\n",
        "df['neg'] = [analyzer.polarity_scores(x)['neg'] for x in df['body_pp_4']]\n",
        "df['neu'] = [analyzer.polarity_scores(x)['neu'] for x in df['body_pp_4']]\n",
        "df['pos'] = [analyzer.polarity_scores(x)['pos'] for x in df['body_pp_4']]"
      ],
      "metadata": {
        "id": "E1vXosP3CBqM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "df"
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        },
        "id": "CSQcSF_EPBh8",
        "outputId": "9ba5b577-c843-4674-b209-b69c5b0e5899"
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      "execution_count": null,
      "outputs": [
        {
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              "      <td>hnt38as</td>\n",
              "      <td>The_Limpet</td>\n",
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              "      <td>criticalrole</td>\n",
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              "      <td>BlackeeGreen</td>\n",
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            "text/plain": [
              "             id              author  ...    neu    pos\n",
              "0       hnlyzwu          sealandair  ...  0.733  0.000\n",
              "1       hnlj73o  clementjohnson1963  ...  0.185  0.000\n",
              "2       hnn53y2     VolcanicBosnian  ...  0.789  0.000\n",
              "3       hnn6z8j     AussieCollector  ...  0.503  0.146\n",
              "4       hnn0wc9               hu_he  ...  0.415  0.373\n",
              "...         ...                 ...  ...    ...    ...\n",
              "126182  hnt38as          The_Limpet  ...  1.000  0.000\n",
              "126183  hnt0pf0            UncleOok  ...  0.878  0.122\n",
              "126184  hntdj12        BlackeeGreen  ...  0.690  0.310\n",
              "126185  hnugkof      geckothegeek42  ...  0.476  0.524\n",
              "126186  hntah3e       Think-Ad-7612  ...  0.682  0.318\n",
              "\n",
              "[126099 rows x 9 columns]"
            ]
          },
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    {
      "cell_type": "code",
      "source": [
        "print(df['compound'].mean())\n",
        "print(df['neg'].mean())\n",
        "print(df['neu'].mean())\n",
        "print(df['pos'].mean())"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "5D32dQW6Qn6s",
        "outputId": "02f727e9-76d6-4e96-cdf7-07f568938079"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.06087941141484451\n",
            "0.13565076646127366\n",
            "0.6886708459226567\n",
            "0.17463243959110936\n"
          ]
        }
      ]
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    {
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      "source": [
        "df.to_csv('sent10.csv')"
      ],
      "metadata": {
        "id": "LLUkB82dTV7V"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}