Twitter_Sentimental_Analysis.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Twitter Sentimental Analysis.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9XBnBISwBdER",
        "outputId": "e2c80ee0-71a8-48ca-a429-4950ac8e184e"
      },
      "source": [
        "!pip install textblob\n",
        "!pip install tweepy\n",
        "!pip install pycountry\n",
        "!pip install langdetect"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: textblob in /usr/local/lib/python3.7/dist-packages (0.15.3)\n",
            "Requirement already satisfied: nltk>=3.1 in /usr/local/lib/python3.7/dist-packages (from textblob) (3.2.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk>=3.1->textblob) (1.15.0)\n",
            "Requirement already satisfied: tweepy in /usr/local/lib/python3.7/dist-packages (3.10.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from tweepy) (1.15.0)\n",
            "Requirement already satisfied: requests[socks]>=2.11.1 in /usr/local/lib/python3.7/dist-packages (from tweepy) (2.23.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from tweepy) (1.3.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy) (2021.5.30)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6; extra == \"socks\" in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy) (1.7.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->tweepy) (3.1.1)\n",
            "Collecting pycountry\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/76/73/6f1a412f14f68c273feea29a6ea9b9f1e268177d32e0e69ad6790d306312/pycountry-20.7.3.tar.gz (10.1MB)\n",
            "\u001b[K     |████████████████████████████████| 10.1MB 6.9MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: pycountry\n",
            "  Building wheel for pycountry (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pycountry: filename=pycountry-20.7.3-py2.py3-none-any.whl size=10746883 sha256=a1a2aa4da03dec642b3e6264e618c1b8d435ebdeefdb7d92d9ee8edb6691935d\n",
            "  Stored in directory: /root/.cache/pip/wheels/33/4e/a6/be297e6b83567e537bed9df4a93f8590ec01c1acfbcd405348\n",
            "Successfully built pycountry\n",
            "Installing collected packages: pycountry\n",
            "Successfully installed pycountry-20.7.3\n",
            "Collecting langdetect\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0e/72/a3add0e4eec4eb9e2569554f7c70f4a3c27712f40e3284d483e88094cc0e/langdetect-1.0.9.tar.gz (981kB)\n",
            "\u001b[K     |████████████████████████████████| 983kB 6.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from langdetect) (1.15.0)\n",
            "Building wheels for collected packages: langdetect\n",
            "  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for langdetect: filename=langdetect-1.0.9-cp37-none-any.whl size=993242 sha256=f1c8cff8fad2b4e0a1ab45f48762b6203976af80e433c440ddfed17ede3c8ffa\n",
            "  Stored in directory: /root/.cache/pip/wheels/7e/18/13/038c34057808931c7ddc6c92d3aa015cf1a498df5a70268996\n",
            "Successfully built langdetect\n",
            "Installing collected packages: langdetect\n",
            "Successfully installed langdetect-1.0.9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WDCCOhosNsa4"
      },
      "source": [
        "### Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "szVc3kJMCR1e",
        "outputId": "2269e567-9a51-4c79-fb4d-022694cbb731"
      },
      "source": [
        "from textblob import TextBlob\n",
        "import sys\n",
        "import tweepy\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import nltk\n",
        "nltk.download('vader_lexicon')\n",
        "nltk.download('stopwords')\n",
        "import pycountry\n",
        "import re\n",
        "import string\n",
        "\n",
        "from wordcloud import WordCloud, STOPWORDS\n",
        "from PIL import Image\n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "from langdetect import detect\n",
        "from nltk.stem import SnowballStemmer\n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "from sklearn.feature_extraction.text import CountVectorizer"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/nltk/twitter/__init__.py:20: UserWarning: The twython library has not been installed. Some functionality from the twitter package will not be available.\n",
            "  warnings.warn(\"The twython library has not been installed. \"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fFGakE88YP59"
      },
      "source": [
        "## Authentication for Twitter API\n",
        "\n",
        "\n",
        "> Setting all Twitter API config variables required for authentication with Tweepy.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_KkeSHFeCRy8"
      },
      "source": [
        "consumerKey = \"qh0PEi9LGbFqrq0bEVx10Eg2d\"\n",
        "consumerSecret = \"hoAuH6TQkhLFSBgV50IlwMbYIgZiRr0l6dz6dZHXWcZu8KRIYx\"\n",
        "accessToken = \"1403975818217357326-dtrPf2smIz8TptyRXpM3faTCV89HN2\"\n",
        "accessTokenSecret = \"9vgGQEW532KDDPOrWsGS89JjGmnoT7nu7rN84az99g2pL\"\n",
        "auth = tweepy.OAuthHandler(consumerKey, consumerSecret)\n",
        "auth.set_access_token(accessToken, accessTokenSecret)\n",
        "api = tweepy.API(auth)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "96zvh6e7Zt0J"
      },
      "source": [
        "## Getting Tweets With Keyword or Hashtag"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2D9i7iQZ1x4"
      },
      "source": [
        "### Sentiment Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ErKeIDTcCRwc",
        "outputId": "a19ca25e-cdf4-4c1d-f18a-17806a530f50"
      },
      "source": [
        "def percentage(part,whole):\n",
        " return 100 * float(part)/float(whole)\n",
        "keyword = input(\"Please enter keyword or hashtag to search: \")\n",
        "noOfTweet = int(input (\"Please enter how many tweets to analyze: \"))\n",
        "tweets = tweepy.Cursor(api.search, q=keyword).items(noOfTweet)\n",
        "positive = 0\n",
        "negative = 0\n",
        "neutral = 0\n",
        "polarity = 0\n",
        "tweet_list = []\n",
        "neutral_list = []\n",
        "negative_list = []\n",
        "positive_list = []\n",
        "for tweet in tweets:\n",
        " \n",
        " #print(tweet.text)\n",
        " tweet_list.append(tweet.text)\n",
        " analysis = TextBlob(tweet.text)\n",
        " score = SentimentIntensityAnalyzer().polarity_scores(tweet.text)\n",
        " neg = score['neg']\n",
        " neu = score['neu']\n",
        " pos = score['pos']\n",
        " comp = score['compound']\n",
        " polarity += analysis.sentiment.polarity\n",
        " \n",
        " if neg > pos:\n",
        "  negative_list.append(tweet.text)\n",
        "  negative += 1\n",
        " elif pos > neg:\n",
        "  positive_list.append(tweet.text)\n",
        "  positive += 1\n",
        " \n",
        " elif pos == neg:\n",
        "  neutral_list.append(tweet.text)\n",
        "  neutral += 1\n",
        "positive = percentage(positive, noOfTweet)\n",
        "negative = percentage(negative, noOfTweet)\n",
        "neutral = percentage(neutral, noOfTweet)\n",
        "polarity = percentage(polarity, noOfTweet)\n",
        "positive = format(positive, '.1f')\n",
        "negative = format(negative, '.1f')\n",
        "neutral = format(neutral, '.1f')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Please enter keyword or hashtag to search: BTS\n",
            "Please enter how many tweets to analyze: 2500\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Utc2cIGlCRtl",
        "outputId": "f33d86e1-0fe4-40c3-f758-6c191b93d79d"
      },
      "source": [
        "#Number of Tweets (Total, Positive, Negative, Neutral)\n",
        "tweet_list = pd.DataFrame(tweet_list)\n",
        "neutral_list = pd.DataFrame(neutral_list)\n",
        "negative_list = pd.DataFrame(negative_list)\n",
        "positive_list = pd.DataFrame(positive_list)\n",
        "print(\"total number: \",len(tweet_list))\n",
        "print(\"positive sentiment: \",len(positive_list))\n",
        "print(\"negative sentiment: \", len(negative_list))\n",
        "print(\"neutral sentiment: \",len(neutral_list))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total number:  2500\n",
            "positive sentiment:  549\n",
            "negative sentiment:  189\n",
            "neutral sentiment:  1762\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AcCl-i88lk3X"
      },
      "source": [
        "## Creating PieCart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "oNkChWUUCRps",
        "outputId": "7572b581-24f0-47f6-d0a6-19d69f87b618"
      },
      "source": [
        "labels = ['Positive ['+str(positive)+'%]' , 'Neutral ['+str(neutral)+'%]','Negative ['+str(negative)+'%]']\n",
        "sizes = [positive, neutral, negative]\n",
        "colors = ['yellowgreen', 'purple','yellow']\n",
        "patches, texts = plt.pie(sizes,colors=colors, startangle=90)\n",
        "plt.style.use('default')\n",
        "plt.legend(labels)\n",
        "plt.title(\"Sentiment Analysis Result for keyword = \"+keyword+\"\" )\n",
        "plt.axis('equal')\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "3bZLWjW4CRit",
        "outputId": "1c8b9109-b6af-4817-a84f-e3eb84646297"
      },
      "source": [
        "tweet_list"
      ],
      "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RT @jimintoday__: [Teaser] BTS X 서울 E-프리 2022 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RT @charts_k: [Teaser] #BTS X 서울 E-프리 2022 (SE...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RT @BRILLER__613: [Teaser] BTS X 서울 E-프리 2022 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RT @Army_Connect: We crossed 10K total orders🥳...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RT @PJM_data: Emmy Award Winning Comedian John...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2495</th>\n",
              "      <td>RT @ONLY_MOMENT__V: Fila 인스타스토리 (1)\\n\\n#방탄소년단 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2496</th>\n",
              "      <td>RT @NicoMar97: K-Media: \"El Campeonato Mundial...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2497</th>\n",
              "      <td>@MyMoonJin_SG @BTS_twt ojala que si!!!! pero n...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2498</th>\n",
              "      <td>RT @BTSsilver_voice: 월와핸과 세일미가 있는 풍경\\n미쳤다 그냥\\n...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2499</th>\n",
              "      <td>RT @comma_bts: [scan] 소우주 미니포카 드볼 (1)\\nSOWOOZO...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2500 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                      0\n",
              "0     RT @jimintoday__: [Teaser] BTS X 서울 E-프리 2022 ...\n",
              "1     RT @charts_k: [Teaser] #BTS X 서울 E-프리 2022 (SE...\n",
              "2     RT @BRILLER__613: [Teaser] BTS X 서울 E-프리 2022 ...\n",
              "3     RT @Army_Connect: We crossed 10K total orders🥳...\n",
              "4     RT @PJM_data: Emmy Award Winning Comedian John...\n",
              "...                                                 ...\n",
              "2495  RT @ONLY_MOMENT__V: Fila 인스타스토리 (1)\\n\\n#방탄소년단 ...\n",
              "2496  RT @NicoMar97: K-Media: \"El Campeonato Mundial...\n",
              "2497  @MyMoonJin_SG @BTS_twt ojala que si!!!! pero n...\n",
              "2498  RT @BTSsilver_voice: 월와핸과 세일미가 있는 풍경\\n미쳤다 그냥\\n...\n",
              "2499  RT @comma_bts: [scan] 소우주 미니포카 드볼 (1)\\nSOWOOZO...\n",
              "\n",
              "[2500 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rOAQpZ66nRx2"
      },
      "source": [
        "## Cleaning Tweets to Analyse Sentiment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rmSpQjHCnUTM"
      },
      "source": [
        "tweet_list.drop_duplicates(inplace = True)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VZmp9KujngD2"
      },
      "source": [
        "### Cleaning Text (RT, Punctuation etc), Creating new dataframe and new features, Removing RT, Punctuation etc"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yUkM2CoFDMme"
      },
      "source": [
        "\n",
        "\n",
        "> Before starting the sentiment analysis it is a good idea to clean up each tweets from an unnecessary data first.\n",
        "\n",
        "\n",
        "1. To remove mentions\n",
        "2. To remove hashtags\n",
        "3. To remove retweets\n",
        "4. To remove urls\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "2kXGeLOfnUQ-",
        "outputId": "4ad10f78-0d9e-4350-caa7-24c15354056d"
      },
      "source": [
        "tw_list = pd.DataFrame(tweet_list)\n",
        "tw_list[\"text\"] = tw_list[0]\n",
        "\n",
        "remove_rt = lambda x: re.sub('RT @\\w+: ',\" \",x)\n",
        "rt = lambda x: re.sub(\"(@[A-Za-z0–9]+)|([-⁰9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\",\" \",x)\n",
        "tw_list[\"text\"] = tw_list.text.map(remove_rt).map(rt)\n",
        "tw_list[\"text\"] = tw_list.text.str.lower()\n",
        "tw_list.head(10)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RT @jimintoday__: [Teaser] BTS X 서울 E-프리 2022 ...</td>\n",
              "      <td>[      ]       서울   프리 2022 (             202...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RT @charts_k: [Teaser] #BTS X 서울 E-프리 2022 (SE...</td>\n",
              "      <td>[      ] #      서울   프리 2022 (             20...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RT @BRILLER__613: [Teaser] BTS X 서울 E-프리 2022 ...</td>\n",
              "      <td>[      ]       서울   프리 2022 (             202...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RT @Army_Connect: We crossed 10K total orders🥳...</td>\n",
              "      <td>10              🥳🎉🥳\\n\\n⏰∼2        ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RT @PJM_data: Emmy Award Winning Comedian John...</td>\n",
              "      <td>,     ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>RT @ARMYTEAMIID: [🎥]\\n\\nBTS x SMASH APP\\n#방탄소년...</td>\n",
              "      <td>[🎥]\\n\\n               \\n#방탄소년단 #     _       ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>RT @bts_bighit: Are you all excited for #BTS_B...</td>\n",
              "      <td>#   _                ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>RT @allangyi: 달다구리솜 @_thal_0 님카페 놀러왔어요^^ \\n예쁜 ...</td>\n",
              "      <td>달다구리솜 @_    _0 님카페 놀러왔어요^^ \\n예쁜 컵홀더랑 선물도 받고 귀...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>RT @bts_nanak: SONGS 방탄 멘트\\n#방탄소년단 #BTS @BTS_t...</td>\n",
              "      <td>방탄 멘트\\n#방탄소년단 #     _         :// .  / ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>RT @sunflowercharts: ✅ 364.76 ล้านวิว\\n\\nเป้าห...</td>\n",
              "      <td>✅ 364.76 ล้านวิว\\n\\nเป้าหมายต่อไป: 400 ล้านวิ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   0                                               text\n",
              "0  RT @jimintoday__: [Teaser] BTS X 서울 E-프리 2022 ...   [      ]       서울   프리 2022 (             202...\n",
              "1  RT @charts_k: [Teaser] #BTS X 서울 E-프리 2022 (SE...   [      ] #      서울   프리 2022 (             20...\n",
              "2  RT @BRILLER__613: [Teaser] BTS X 서울 E-프리 2022 ...   [      ]       서울   프리 2022 (             202...\n",
              "3  RT @Army_Connect: We crossed 10K total orders🥳...              10              🥳🎉🥳\\n\\n⏰∼2        ...\n",
              "4  RT @PJM_data: Emmy Award Winning Comedian John...                                          ,     ...\n",
              "5  RT @ARMYTEAMIID: [🎥]\\n\\nBTS x SMASH APP\\n#방탄소년...   [🎥]\\n\\n               \\n#방탄소년단 #     _       ...\n",
              "6  RT @bts_bighit: Are you all excited for #BTS_B...                           #   _                ...\n",
              "7  RT @allangyi: 달다구리솜 @_thal_0 님카페 놀러왔어요^^ \\n예쁜 ...   달다구리솜 @_    _0 님카페 놀러왔어요^^ \\n예쁜 컵홀더랑 선물도 받고 귀...\n",
              "8  RT @bts_nanak: SONGS 방탄 멘트\\n#방탄소년단 #BTS @BTS_t...         방탄 멘트\\n#방탄소년단 #     _         :// .  / ...\n",
              "9  RT @sunflowercharts: ✅ 364.76 ล้านวิว\\n\\nเป้าห...   ✅ 364.76 ล้านวิว\\n\\nเป้าหมายต่อไป: 400 ล้านวิ..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SVUB-fOGoYgn"
      },
      "source": [
        "## Sentiment Analysis\n",
        "\n",
        "\n",
        "> Calculating Negative, Positive, Neutral and Compound values\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "_R4ZA-STnUOi",
        "outputId": "4eb943e7-75e5-471c-cffc-f8ca3aa1dfae"
      },
      "source": [
        "tw_list[['polarity', 'subjectivity']] = tw_list['text'].apply(lambda Text: pd.Series(TextBlob(Text).sentiment))\n",
        "for index, row in tw_list['text'].iteritems():\n",
        " score = SentimentIntensityAnalyzer().polarity_scores(row)\n",
        " neg = score['neg']\n",
        " neu = score['neu']\n",
        " pos = score['pos']\n",
        " comp = score['compound']\n",
        "\n",
        " if neg > pos:\n",
        "   tw_list.loc[index, 'sentiment'] = \"negative\"\n",
        " elif pos > neg:\n",
        "   tw_list.loc[index, 'sentiment'] = \"positive\"\n",
        " else:\n",
        "   tw_list.loc[index, 'sentiment'] = \"neutral\"\n",
        "   tw_list.loc[index, 'neg'] = neg\n",
        "   tw_list.loc[index, 'neu'] = neu\n",
        "   tw_list.loc[index, 'pos'] = pos\n",
        "   tw_list.loc[index, 'compound'] = comp\n",
        "tw_list.head(10)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>0</th>\n",
              "      <td>RT @jimintoday__: [Teaser] BTS X 서울 E-프리 2022 ...</td>\n",
              "      <td>[      ]       서울   프리 2022 (             202...</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RT @charts_k: [Teaser] #BTS X 서울 E-프리 2022 (SE...</td>\n",
              "      <td>[      ] #      서울   프리 2022 (             20...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RT @BRILLER__613: [Teaser] BTS X 서울 E-프리 2022 ...</td>\n",
              "      <td>[      ]       서울   프리 2022 (             202...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RT @Army_Connect: We crossed 10K total orders🥳...</td>\n",
              "      <td>10              🥳🎉🥳\\n\\n⏰∼2        ...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RT @PJM_data: Emmy Award Winning Comedian John...</td>\n",
              "      <td>,     ...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>neutral</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>RT @ARMYTEAMIID: [🎥]\\n\\nBTS x SMASH APP\\n#방탄소년...</td>\n",
              "      <td>[🎥]\\n\\n               \\n#방탄소년단 #     _       ...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>RT @bts_bighit: Are you all excited for #BTS_B...</td>\n",
              "      <td>#   _                ...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>RT @allangyi: 달다구리솜 @_thal_0 님카페 놀러왔어요^^ \\n예쁜 ...</td>\n",
              "      <td>달다구리솜 @_    _0 님카페 놀러왔어요^^ \\n예쁜 컵홀더랑 선물도 받고 귀...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>RT @bts_nanak: SONGS 방탄 멘트\\n#방탄소년단 #BTS @BTS_t...</td>\n",
              "      <td>방탄 멘트\\n#방탄소년단 #     _         :// .  / ...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>RT @sunflowercharts: ✅ 364.76 ล้านวิว\\n\\nเป้าห...</td>\n",
              "      <td>✅ 364.76 ล้านวิว\\n\\nเป้าหมายต่อไป: 400 ล้านวิ...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   0  ... compound\n",
              "0  RT @jimintoday__: [Teaser] BTS X 서울 E-프리 2022 ...  ...      0.0\n",
              "1  RT @charts_k: [Teaser] #BTS X 서울 E-프리 2022 (SE...  ...      0.0\n",
              "2  RT @BRILLER__613: [Teaser] BTS X 서울 E-프리 2022 ...  ...      0.0\n",
              "3  RT @Army_Connect: We crossed 10K total orders🥳...  ...      0.0\n",
              "4  RT @PJM_data: Emmy Award Winning Comedian John...  ...      0.0\n",
              "5  RT @ARMYTEAMIID: [🎥]\\n\\nBTS x SMASH APP\\n#방탄소년...  ...      0.0\n",
              "6  RT @bts_bighit: Are you all excited for #BTS_B...  ...      0.0\n",
              "7  RT @allangyi: 달다구리솜 @_thal_0 님카페 놀러왔어요^^ \\n예쁜 ...  ...      0.0\n",
              "8  RT @bts_nanak: SONGS 방탄 멘트\\n#방탄소년단 #BTS @BTS_t...  ...      0.0\n",
              "9  RT @sunflowercharts: ✅ 364.76 ล้านวิว\\n\\nเป้าห...  ...      0.0\n",
              "\n",
              "[10 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f-UhxlEIpWHJ"
      },
      "source": [
        "\n",
        "\n",
        "> Spliting data frame into 3 groups for all sentiments (positive, negative and neutral)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XHfWOZMlnUL3"
      },
      "source": [
        "tw_list_negative = tw_list[tw_list[\"sentiment\"]==\"negative\"]\n",
        "tw_list_positive = tw_list[tw_list[\"sentiment\"]==\"positive\"]\n",
        "tw_list_neutral = tw_list[tw_list[\"sentiment\"]==\"neutral\"]"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "3jnk171gvQrC",
        "outputId": "f6af1505-0cf5-4ecb-97fc-56ce9efe08a7"
      },
      "source": [
        "def count_values_in_column(data,feature):\n",
        " total=data.loc[:,feature].value_counts(dropna=False)\n",
        " percentage=round(data.loc[:,feature].value_counts(dropna=False,normalize=True)*100,2)\n",
        " return pd.concat([total,percentage],axis=1,keys=['Total','Percentage'])\n",
        "\n",
        "#Count_values for sentiment\n",
        "count_values_in_column(tw_list,\"sentiment\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Total</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>neutral</th>\n",
              "      <td>1388</td>\n",
              "      <td>99.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>positive</th>\n",
              "      <td>7</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>negative</th>\n",
              "      <td>4</td>\n",
              "      <td>0.29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Total  Percentage\n",
              "neutral    1388       99.21\n",
              "positive      7        0.50\n",
              "negative      4        0.29"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kK8EjY9jqA90"
      },
      "source": [
        "\n",
        "\n",
        "> Pie Chart\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "W4wFzSIKp6wM",
        "outputId": "6b8cc6bc-7940-49ff-dccb-a4438ce8c8ab"
      },
      "source": [
        "pichart = count_values_in_column(tw_list,\"sentiment\")\n",
        "names= pichart.index\n",
        "size=pichart[\"Percentage\"]\n",
        " \n",
        "\n",
        "my_circle=plt.Circle( (0,0), 0.7, color='white')\n",
        "plt.pie(size, labels=names, colors=['purple', 'yellow' ,'yellowgreen'])\n",
        "p=plt.gcf()\n",
        "p.gca().add_artist(my_circle)\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7eaSlIZxJbLO"
      },
      "source": [
        "### Calculating the tweet length and word count to see the density of words and characters used in tweets based on different sentiments."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XFnAJ9WxtAxk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "outputId": "4b09853e-ffbd-40a4-f588-bc79b2e9a788"
      },
      "source": [
        "tw_list['text_len'] = tw_list['text'].astype(str).apply(len)\n",
        "tw_list['text_word_count'] = tw_list['text'].apply(lambda x: len(str(x).split()))\n",
        "round(pd.DataFrame(tw_list.groupby(\"sentiment\").text_len.mean()),2)"
      ],
      "execution_count": 14,
      "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_len</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentiment</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>negative</th>\n",
              "      <td>75.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>neutral</th>\n",
              "      <td>92.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>positive</th>\n",
              "      <td>92.14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           text_len\n",
              "sentiment          \n",
              "negative      75.00\n",
              "neutral       92.90\n",
              "positive      92.14"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fVVShr7itAus",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "outputId": "4112a691-3230-40d9-d63c-259f258fccea"
      },
      "source": [
        "round(pd.DataFrame(tw_list.groupby(\"sentiment\").text_word_count.mean()),2)"
      ],
      "execution_count": 15,
      "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_word_count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentiment</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>negative</th>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>neutral</th>\n",
              "      <td>9.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>positive</th>\n",
              "      <td>8.29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           text_word_count\n",
              "sentiment                 \n",
              "negative              4.00\n",
              "neutral               9.59\n",
              "positive              8.29"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ZCizuqGCiLf"
      },
      "source": [
        "## Removing Punctuation and Stopwords and applying Tokenization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XK4Cy5E2tAsF"
      },
      "source": [
        "#punctuation removal\n",
        "def remove_punct(text):\n",
        "    text  = \"\".join([char for char in text if char not in string.punctuation])\n",
        "    text = re.sub('[0-9]+', '', text)\n",
        "    return text\n",
        "\n",
        "tw_list['punct'] = tw_list['text'].apply(lambda x: remove_punct(x))\n",
        "\n",
        "\n",
        "#tokenization\n",
        "def tokenization(text):\n",
        "    text = re.split('\\W+', text)\n",
        "    return text\n",
        "\n",
        "tw_list['tokenized'] = tw_list['punct'].apply(lambda x: tokenization(x.lower()))\n",
        "\n",
        "\n",
        "#Stopwords Removal\n",
        "stopword = nltk.corpus.stopwords.words('english')\n",
        "def remove_stopwords(text):\n",
        "    text = [word for word in text if word not in stopword]\n",
        "    return text\n",
        "    \n",
        "tw_list['nonstop'] = tw_list['tokenized'].apply(lambda x: remove_stopwords(x))"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-CZpEvajFB1m"
      },
      "source": [
        "## Applying stemmer to eliminate words that come from the same root"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VfcrEHWGFGK2"
      },
      "source": [
        "ps = nltk.PorterStemmer()\n",
        "\n",
        "def stemming(text):\n",
        "    text = [ps.stem(word) for word in text]\n",
        "    return text\n",
        "\n",
        "tw_list['stemmed'] = tw_list['nonstop'].apply(lambda x: stemming(x))"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sLBmYq50DWQP"
      },
      "source": [
        "### Text Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CnzL-jqVtApU"
      },
      "source": [
        "def clean_text(text):\n",
        "    text_lc = \"\".join([word.lower() for word in text if word not in string.punctuation]) # remove puntuation\n",
        "    text_rc = re.sub('[0-9]+', '', text_lc)\n",
        "    tokens = re.split('\\W+', text_rc)    # tokenization\n",
        "    text = [ps.stem(word) for word in tokens if word not in stopword]  # remove stopwords \n",
        "    return text"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5B5cSTHFtAlt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "dc089a7e-de89-40bb-c1a3-f08dcf1bbf7b"
      },
      "source": [
        "tw_list.head()"
      ],
      "execution_count": 19,
      "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>0</th>\n",
              "      <th>text</th>\n",
              "      <th>polarity</th>\n",
              "      <th>subjectivity</th>\n",
              "      <th>sentiment</th>\n",
              "      <th>neg</th>\n",
              "      <th>neu</th>\n",
              "      <th>pos</th>\n",
              "      <th>compound</th>\n",
              "      <th>text_len</th>\n",
              "      <th>text_word_count</th>\n",
              "      <th>punct</th>\n",
              "      <th>tokenized</th>\n",
              "      <th>nonstop</th>\n",
              "      <th>stemmed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RT @jimintoday__: [Teaser] BTS X 서울 E-프리 2022 ...</td>\n",
              "      <td>[      ]       서울   프리 2022 (             202...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>120</td>\n",
              "      <td>19</td>\n",
              "      <td>서울   프리                      \\n ...</td>\n",
              "      <td>[, 서울, 프리, 방탄소년단, ]</td>\n",
              "      <td>[, 서울, 프리, 방탄소년단, ]</td>\n",
              "      <td>[, 서울, 프리, 방탄소년단, ]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RT @charts_k: [Teaser] #BTS X 서울 E-프리 2022 (SE...</td>\n",
              "      <td>[      ] #      서울   프리 2022 (             20...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>98</td>\n",
              "      <td>17</td>\n",
              "      <td>서울   프리               \\n\\n      ...</td>\n",
              "      <td>[, 서울, 프리, ]</td>\n",
              "      <td>[, 서울, 프리, ]</td>\n",
              "      <td>[, 서울, 프리, ]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RT @BRILLER__613: [Teaser] BTS X 서울 E-프리 2022 ...</td>\n",
              "      <td>[      ]       서울   프리 2022 (             202...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>105</td>\n",
              "      <td>16</td>\n",
              "      <td>서울   프리                      \\n ...</td>\n",
              "      <td>[, 서울, 프리, ]</td>\n",
              "      <td>[, 서울, 프리, ]</td>\n",
              "      <td>[, 서울, 프리, ]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RT @Army_Connect: We crossed 10K total orders🥳...</td>\n",
              "      <td>10              🥳🎉🥳\\n\\n⏰∼2        ...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>123</td>\n",
              "      <td>9</td>\n",
              "      <td>🥳🎉🥳\\n\\n⏰∼           ...</td>\n",
              "      <td>[, ]</td>\n",
              "      <td>[, ]</td>\n",
              "      <td>[, ]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RT @PJM_data: Emmy Award Winning Comedian John...</td>\n",
              "      <td>,     ...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>127</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>[, ]</td>\n",
              "      <td>[, ]</td>\n",
              "      <td>[, ]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   0  ...              stemmed\n",
              "0  RT @jimintoday__: [Teaser] BTS X 서울 E-프리 2022 ...  ...  [, 서울, 프리, 방탄소년단, ]\n",
              "1  RT @charts_k: [Teaser] #BTS X 서울 E-프리 2022 (SE...  ...         [, 서울, 프리, ]\n",
              "2  RT @BRILLER__613: [Teaser] BTS X 서울 E-프리 2022 ...  ...         [, 서울, 프리, ]\n",
              "3  RT @Army_Connect: We crossed 10K total orders🥳...  ...                 [, ]\n",
              "4  RT @PJM_data: Emmy Award Winning Comedian John...  ...                 [, ]\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GgRZlyz_nRqc"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}