Twitter_sentiment__analysis.ipynb

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      "authorship_tag": "ABX9TyPXbtggtQ+h3nimMRrztqGk",
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        "<a href=\"https://colab.research.google.com/github/Anupa-123/Twitter_sentiment_analysis/blob/main/Twitter_sentiment__analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "pip install secret"
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            "Building wheels for collected packages: secret\n",
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            "  Created wheel for secret: filename=secret-0.8-py3-none-any.whl size=9099 sha256=f7fbac03be487915c094e70ffa3fa6fd40729d76db869b85165dc556a199b4d3\n",
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            "Collecting oauth2\n",
            "  Downloading oauth2-1.9.0.post1-py2.py3-none-any.whl (25 kB)\n",
            "Requirement already satisfied: httplib2 in /usr/local/lib/python3.9/dist-packages (from oauth2) (0.21.0)\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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      "cell_type": "code",
      "source": [
        "pip install twitter"
      ],
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      "execution_count": 5,
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting twitter\n",
            "  Downloading twitter-1.19.6-py2.py3-none-any.whl (50 kB)\n",
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            "\u001b[?25hRequirement already satisfied: certifi in /usr/local/lib/python3.9/dist-packages (from twitter) (2022.12.7)\n",
            "Installing collected packages: twitter\n",
            "Successfully installed twitter-1.19.6\n"
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      "cell_type": "code",
      "source": [
        "pip install *"
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      "metadata": {
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        "id": "0N-i6gQvYoE7",
        "outputId": "4a0b9ed6-1914-4ce7-b7b6-fb6f99bc4693"
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      "execution_count": 6,
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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    {
      "cell_type": "code",
      "source": [
        "from flask import Flask\n",
        "from flask import Flask, flash, redirect, render_template, request, session, abort\n",
        "from flask import Markup\n",
        "import os"
      ],
      "metadata": {
        "id": "AtRr92XUdCid"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "app = Flask(__name__)\n",
        "app.config['SESSION_TYPE'] = 'filesystem'\n",
        "app.config['SECRET_KEY'] = 'reds209ndsldssdsljdsldsdsljdsldksdksdsdfsfsfsfis'\n",
        "#session.init_app(app)"
      ],
      "metadata": {
        "id": "F2LX0_rAdLLn"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "positive = 0\n",
        "negative = 0\n",
        "neutral = 0"
      ],
      "metadata": {
        "id": "JRJkSdN-dLGR"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@app.route('/')\n",
        "def home():\n",
        "    if not session.get('searched'):\n",
        "        return render_template('search.html')\n",
        "    else:\n",
        "        labels = [\"Positive\", \"Negative\", \"Neutral\"]\n",
        "        global positive\n",
        "        global negative\n",
        "        global neutral\n",
        "        values = [positive, negative, neutral]\n",
        "        colors = [\"#8bc34a\", \"#ff5252\", \"#9e9e9e\"]\n",
        "        session['searched'] = False\n",
        "        return render_template('chart.html', set=zip(values, labels, colors))"
      ],
      "metadata": {
        "id": "IzeH-2QldMiQ"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@app.route('/search', methods=['POST'])\n",
        "def do_search():\n",
        "    if request.form['search_query'] == '':\n",
        "        flash('Search Queary cannot be empty!')\n",
        "        session['searched'] = False\n",
        "    if request.form['max_tweets'] == '':\n",
        "        flash('Max Tweets cannot be empty!')\n",
        "        session['searched'] = False\n",
        "    else:\n",
        "        if not request.form['max_tweets'].isdigit():\n",
        "            flash('Max Tweets should be a number!')\n",
        "            session['searched'] = False\n",
        "        else:\n",
        "            if int(request.form['max_tweets']) > 0 & int(request.form['max_tweets']) <= 100000:\n",
        "                import tweepy\n",
        "                import sys\n",
        "                import csv\n",
        "                import pickle\n",
        "                from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "                from collections import Counter\n",
        "\n",
        "                # importing the auth variables from secret.py\n",
        "                import secret\n",
        "\n",
        "                # authenicating with authentication variables\n",
        "                auth = tweepy.OAuthHandler(secret.consumer_key, secret.consumer_secret)\n",
        "                auth.set_access_token(secret.access_token, secret.access_token_secret)\n",
        "                api = tweepy.API(auth, wait_on_rate_limit=True,\n",
        "                                 wait_on_rate_limit_notify=True)\n",
        "                # Error handling\n",
        "                if (not api):\n",
        "                    print(\"Problem Connecting to API\")\n",
        "\n",
        "                # inputs for counts taken\n",
        "                hash_tag = request.form['search_query']\n",
        "                number = int(request.form['max_tweets'])\n",
        "                # location = input(\"What is the location of party? \\n\")\n",
        "\n",
        "                # # Getting Geo ID for Places\n",
        "                # places = api.geo_search(query=location)\n",
        "                # # place id\n",
        "                # print(places[0])\n",
        "\n",
        "                tweetsPerQry = 100  # this is the max the API permits\n",
        "\n",
        "                # If results from a specific ID onwards are reqd, set since_id to that ID.\n",
        "                # else default to no lower limit, go as far back as API allows\n",
        "                sinceId = None\n",
        "\n",
        "                # If results only below a specific ID are, set max_id to that ID.\n",
        "                # else default to no upper limit, start from the most recent tweet matching the search query.\n",
        "                max_id = -1\n",
        "                data = None\n",
        "\n",
        "                dataset = []\n",
        "                outputFile = 'test.data'\n",
        "                fw = open(outputFile, 'wb')\n",
        "\n",
        "                tweetCount = 0\n",
        "                print(\"Downloading max {0} tweets\".format(number))\n",
        "                while tweetCount < number:\n",
        "                    try:\n",
        "                        if (max_id <= 0):\n",
        "                            if (not sinceId):\n",
        "                                new_tweets = api.search(q=hash_tag, count=tweetsPerQry, tweet_mode='extended', lang='en')\n",
        "                                tweetCount += len(new_tweets)\n",
        "                                print(\"Downloaded {0} tweets\".format(tweetCount))\n",
        "                            else:\n",
        "                                new_tweets = api.search(q=hash_tag, count=tweetsPerQry,\n",
        "                                                        since_id=sinceId, tweet_mode='extended', lang='en')\n",
        "                                print(\"here 2\")\n",
        "                        else:\n",
        "                            if (not sinceId):\n",
        "                                new_tweets = api.search(q=hash_tag, count=tweetsPerQry,\n",
        "                                                        max_id=str(max_id - 1), tweet_mode='extended', lang='en')\n",
        "                                print(\"here 3\")\n",
        "                            else:\n",
        "                                new_tweets = api.search(q=hash_tag, count=tweetsPerQry,\n",
        "                                                        max_id=str(max_id - 1),\n",
        "                                                        since_id=sinceId, tweet_mode='extended', lang='en')\n",
        "                                print(\"here 4\")\n",
        "                            if not new_tweets:\n",
        "                                print(\"No more tweets found\")\n",
        "                                break\n",
        "\n",
        "                        # print(new_tweets)\n",
        "\n",
        "                        tweets = new_tweets\n",
        "                        for tweet in tweets:\n",
        "                            dataset.append(tweet.full_text)\n",
        "                        #data += pd.DataFrame(data=[tweet.text for tweet in tweets], columns=['Tweets'])\n",
        "                        tweetCount += len(new_tweets)\n",
        "                        print(\"Downloaded {0} tweets\".format(tweetCount))\n",
        "                        max_id = new_tweets[-1].id\n",
        "                    except tweepy.TweepError as e:\n",
        "                        print(str(e))\n",
        "                        break\n",
        "                    \n",
        "                pickle.dump(dataset, fw)\n",
        "                fw.close()\n",
        "\n",
        "                inputFile = 'test.data'\n",
        "                fd = open(inputFile, 'rb')\n",
        "                dataset = pickle.load(fd)\n",
        "                print(dataset)\n",
        "\n"
      ],
      "metadata": {
        "id": "hWmcV7cAdS0w"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " # search tweets with some keywords\n",
        "                # results = api.search(hash_tag,\n",
        "                #                      count=number,\n",
        "                #                      since=\"2019-03-04\",\n",
        "                #                    until=\"2019-03-05\",\n",
        "                #                      tweet_mode='extended',\n",
        "                #                      lang='en',\n",
        "                #                      place=places[0].id\n",
        "                #                      )\n",
        "\n",
        "                # print (results)\n",
        "\n",
        "                # tweets = results\n",
        "                # data = pd.DataFrame(data=[tweet.full_text for tweet in tweets], columns=['Tweets'])\n",
        "                # data.to_csv('output.csv')\n",
        "\n",
        "                # print the first 10 data\n",
        "                # print(data)"
      ],
      "metadata": {
        "id": "bctzS3YpdozR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk.sentiment import SentimentIntensityAnalyzer\n",
        "nltk.download('vader_lexicon')\n",
        "from collections import Counter\n",
        "\n",
        "def home():\n",
        "    pass\n",
        "\n",
        "sid = SentimentIntensityAnalyzer()\n",
        "dataset = [\"I love this product!\", \"This product is terrible.\", \"This product is just okay.\"]\n",
        "l = []\n",
        "counter = Counter()\n",
        "for data in dataset:\n",
        "    ss = sid.polarity_scores(data)\n",
        "    l.append(ss)\n",
        "    k = ss['compound']\n",
        "    if k >= 0.05:\n",
        "        counter['positive'] += 1\n",
        "    elif k <= -0.05:\n",
        "        counter['negative'] += 1\n",
        "    else:\n",
        "        counter['neutral'] += 1\n",
        "\n",
        "positive = counter['positive']\n",
        "negative = counter['negative']\n",
        "neutral = counter['neutral']\n",
        "\n",
        "app.secret_key = 'abcdefghijk'\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    #session['searched'] = True\n",
        "    app.run(debug=True, host='0.0.0.0', port=4000)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5MU5eZAXf-Ck",
        "outputId": "cbb7d3d8-9f78-4ef5-d744-2ae496810a4d"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " * Serving Flask app '__main__'\n",
            " * Debug mode: on\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
            "INFO:werkzeug:\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
            " * Running on all addresses (0.0.0.0)\n",
            " * Running on http://127.0.0.1:4000\n",
            " * Running on http://172.28.0.12:4000\n",
            "INFO:werkzeug:\u001b[33mPress CTRL+C to quit\u001b[0m\n",
            "INFO:werkzeug: * Restarting with stat\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pIVpXzOpembp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import config"
      ],
      "metadata": {
        "id": "nfi93DeqSxso"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "glLu4IBCFh64"
      },
      "outputs": [],
      "source": [
        "import tweepy\n",
        "import pandas as pd\n",
        "import sys\n",
        "import csv\n",
        "import pickle\n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "import matplotlib.pyplot as plt\n",
        "from collections import Counter"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if sys.version_info[0] < 3:\n",
        "    input = raw_input"
      ],
      "metadata": {
        "id": "XaWeAzHkFlpU"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tweepy\n",
        "import time\n",
        "import pycurl\n",
        "import urllib\n",
        "import json\n",
        "import oauth2 as oauth"
      ],
      "metadata": {
        "id": "v7J8z2dwJDcB"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# importing the auth variables from secret.py\n",
        "import secret"
      ],
      "metadata": {
        "id": "np3XcNXUFljS"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import twitter"
      ],
      "metadata": {
        "id": "U3HA9v_CTycC"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "sys.path.append(\"/path/to/config.config/module\")\n"
      ],
      "metadata": {
        "id": "hjJEkWUiSOM4"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#from config.config import consumer_secret, consumer_key, secret, token\n",
        "import tweepy\n",
        "from tweepy.auth import OAuthHandler\n",
        "from tweepy import API\n",
        "from tweepy import OAuthHandler"
      ],
      "metadata": {
        "id": "eesbYMEfReVf"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tweepy\n",
        "consumer_key = 'your_consumer_key'\n",
        "consumer_secret = 'your_consumer_secret'\n",
        "access_token = 'your_access_token'\n",
        "access_token_secret = 'your_access_token_secret'\n",
        "\n",
        "# Authenicating with authentication variables\n",
        "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
        "auth.set_access_token(access_token, access_token_secret)\n",
        "api = tweepy.API(auth=None, cache=None, host='api.twitter.com', parser=None, \n",
        "                 proxy=None, retry_count=0, retry_delay=0, retry_errors=None, \n",
        "                 timeout=60, upload_host='upload.twitter.com', user_agent=None, wait_on_rate_limit=False)\n",
        "\n",
        "# Error handling\n",
        "try:\n",
        "    api.verify_credentials()\n",
        "    print(\"Authentication OK\")\n",
        "except Exception as e:\n",
        "    print(\"Error during authentication:\", str(e))\n",
        "    exit()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KxmPoaH9WSWK",
        "outputId": "245b6ca3-dd49-46db-94a7-eee2a8bcf22d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error during authentication: Authentication required!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# inputs for counts taken\n",
        "hash_tag = input(\"Which party you want to search ? \\n\")\n",
        "number = int(input(\"How many Tweets do you want to analyze? \\n\"))\n",
        "location = input(\"What is the location of party? \\n\")\n",
        "\n",
        "# Getting Geo ID for Places\n",
        "places = api.geo_search(query=location)\n",
        "# place id\n",
        "print(places[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "jfkEtPMRWSHs",
        "outputId": "4f886eab-0a90-4c0e-b11e-8c18a06b0d17"
      },
      "execution_count": 1,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Which party you want to search ? \n",
            "bjp\n",
            "How many Tweets do you want to analyze? \n",
            "30\n",
            "What is the location of party? \n",
            "mumbai\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-a8b501f5282d>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Getting Geo ID for Places\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mplaces\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mapi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeo_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;31m# place id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplaces\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'api' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "tweetsPerQry = 100  # this is the max the API permits"
      ],
      "metadata": {
        "id": "a7NEkjgrX9Pl"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# If results from a specific ID onwards are reqd, set since_id to that ID.\n",
        "# else default to no lower limit, go as far back as API allows\n",
        "sinceId = None"
      ],
      "metadata": {
        "id": "lgRDXn8XX9p8"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# If results only below a specific ID are, set max_id to that ID.\n",
        "# else default to no upper limit, start from the most recent tweet matching the search query.\n",
        "max_id = -1\n",
        "data = None\n",
        "\n",
        "dataset = []\n",
        "outputFile = 'test.data'\n",
        "fw = open(outputFile, 'wb')"
      ],
      "metadata": {
        "id": "KgGvMnweX-Fc"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tweetCount = 0\n",
        "print(\"Downloading max {0} tweets\".format(number))\n",
        "while tweetCount < number:\n",
        "    try:\n",
        "        if (max_id <= 0):\n",
        "            if (not sinceId):\n",
        "                new_tweets = api.search(q=hash_tag, count=tweetsPerQry, tweet_mode='extended', lang='en')\n",
        "                tweetCount += len(new_tweets)\n",
        "                print(\"Downloaded {0} tweets\".format(tweetCount))\n",
        "            else:\n",
        "                new_tweets = api.search(q=hash_tag, count=tweetsPerQry,\n",
        "                                        since_id=sinceId, tweet_mode='extended', lang='en')\n",
        "                print(\"here 2\")\n",
        "        else:\n",
        "            if (not sinceId):\n",
        "                new_tweets = api.search(q=hash_tag, count=tweetsPerQry,\n",
        "                                        max_id=str(max_id - 1), tweet_mode='extended', lang='en')\n",
        "                print(\"here 3\")\n",
        "            else:\n",
        "                new_tweets = api.search(q=hash_tag, count=tweetsPerQry,\n",
        "                                        max_id=str(max_id - 1),\n",
        "                                        since_id=sinceId, tweet_mode='extended', lang='en')\n",
        "                print(\"here 4\")\n",
        "            if not new_tweets:\n",
        "                print(\"No more tweets found\")\n",
        "                break\n",
        "\n",
        "        # print(new_tweets)\n",
        "\n",
        "        tweets = new_tweets\n",
        "        for tweet in tweets:\n",
        "            dataset.append(tweet.full_text)\n",
        "        #data += pd.DataFrame(data=[tweet.text for tweet in tweets], columns=['Tweets'])\n",
        "        tweetCount += len(new_tweets)\n",
        "        print(\"Downloaded {0} tweets\".format(tweetCount))\n",
        "        max_id = new_tweets[-1].id\n",
        "    except tweepy.TweepError as e:\n",
        "        print(str(e))\n",
        "        break\n",
        "\n",
        "\n",
        "pickle.dump(dataset, fw)\n",
        "fw.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "6ugg7g2zZN-K",
        "outputId": "9a953c10-7179-46bc-f618-2bed7fd79f42"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading max 30 tweets\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-15c22005fad3>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0msinceId\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m                 \u001b[0mnew_tweets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mapi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhash_tag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtweetsPerQry\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtweet_mode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'extended'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlang\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'en'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m                 \u001b[0mtweetCount\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_tweets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'api' is not defined",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-15c22005fad3>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Downloaded {0} tweets\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtweetCount\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0mmax_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_tweets\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m     \u001b[0;32mexcept\u001b[0m \u001b[0mtweepy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTweepError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     38\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'tweepy' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# search tweets with some keywords\n",
        "# results = api.search(hash_tag,\n",
        "#                      count=number,\n",
        "#                      since=\"2019-03-04\",\n",
        "# \t\t\t\t\t until=\"2019-03-05\",\n",
        "#                      tweet_mode='extended',\n",
        "#                      lang='en',\n",
        "#                      place=places[0].id\n",
        "#                      )\n",
        "\n",
        "# print (results)\n",
        "\n",
        "# tweets = results\n",
        "# data = pd.DataFrame(data=[tweet.full_text for tweet in tweets], columns=['Tweets'])\n",
        "# data.to_csv('output.csv')\n",
        "\n",
        "# print the first 10 data\n",
        "# print(data)"
      ],
      "metadata": {
        "id": "XC1_rX3wZS8t"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk.sentiment import SentimentIntensityAnalyzer\n",
        "nltk.download('vader_lexicon')\n",
        "from collections import Counter\n",
        "\n",
        "def home():\n",
        "    pass\n",
        "\n",
        "sid = SentimentIntensityAnalyzer()\n",
        "dataset = [\"I love this product!\", \"This product is terrible.\", \"This product is just okay.\"]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LysAHa43kprR",
        "outputId": "b5b0b7c2-a2f0-4a20-cdc3-f9975e32e1c3"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
            "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "J0L0prx8kx9U"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "l = []\n",
        "counter = Counter()\n",
        "\n",
        "for data in dataset:\n",
        "    ss = sid.polarity_scores(data)\n",
        "    l.append(ss)\n",
        "    k = ss['compound']\n",
        "    if k >= 0.05:\n",
        "        counter['positive'] += 1\n",
        "    elif k <= -0.05:\n",
        "        counter['negative'] += 1\n",
        "    else:\n",
        "        counter['neutral'] += 1\n",
        "\n",
        "positive = counter['positive']\n",
        "negative = counter['negative']\n",
        "neutral = counter['neutral']\n",
        "\n",
        "colors = ['green', 'red', 'grey']\n",
        "sizes = [positive, negative, neutral]\n",
        "labels = 'Positive', 'Negative', 'Neutral'\n",
        "\n",
        "# use matplotlib to plot the chart\n",
        "plt.pie(\n",
        "    x=sizes,\n",
        "    shadow=True,\n",
        "    colors=colors,\n",
        "    labels=labels,\n",
        "    startangle=90,\n",
        "    autopct='%.1f%%'\n",
        ")\n",
        "\n",
        "plt.title(\"Sentiment of {} Tweets about {}\".format(number, hash_tag))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "4aUrRNCdcGKC",
        "outputId": "f2bb2c3d-df53-4a49-c133-ce11997495dd"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Q9iggJAgctuR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "POJQEU39Zb5b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NIy799fNeTHl"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}