twitter_datawithtextblob.ipynb

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  "metadata": {
    "colab": {
      "name": "twitter_data.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMzXboLmts34tpx2C6WokvT",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/redashu/ML/blob/master/twitter_datawithtextblob.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5v6KW3o020nX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# installing  twitter module\n",
        "#!pip  install tweepy\n",
        "import  tweepy "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qFZLFfkD3Nyx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#  defining variables\n",
        "#  consumer key \n",
        "consumer_key=\"kOaUfzK4wgCUtZARHDtge6HZV\"\n",
        "consumer_sec=\"734VPF1vpPHLLovkXBsnNpKP1vYsOXHAnk6ftBbCPAmSU8TfTC\"\n",
        "# access details\n",
        "access_key = access_sec=\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3emgB1N54Yj-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# lets  connect  at  first step auth by using consumer  access details \n",
        "#dir(tweepy)\n",
        "first_auth=tweepy.OAuthHandler(consumer_key,consumer_sec)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3JGeP44K4nM_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#  now setting  second level of auth\n",
        "first_auth.set_access_token(access_key,access_sec)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cRtlqRw55N1V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#  pointing  auth to datastore\n",
        "storage_api_connect=tweepy.API(first_auth,timeout=10)  #  now after this step we can browse / search data from tweetes\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNES3QIs6QO2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4fa6e104-ca51-4df9-fb3f-4a07a823337b"
      },
      "source": [
        "# now  searching  data  as req and waiting  for reply  \n",
        "#  first  method \n",
        "#tweet_data=storage_api_connect.search('corona',count=25)\n",
        "\n",
        "#  second method\n",
        "from textblob  import  TextBlob   #  automated way of sentiment analysis \n",
        "search_data=[\"corona\"]  #  search list\n",
        "list_of_tweets=[]   #  tweet data store\n",
        "\n",
        "if  len(search_data) >= 1 : \n",
        "  for  i  in  tweepy.Cursor(storage_api_connect.search,q=search_data[0]+\"  -filter:retweets\",lang='en',result_type='recent').items(100):\n",
        "    analyse=TextBlob(i.text)\n",
        "    print(analyse.sentiment.polarity)\n",
        "    #print(i.text)\n",
        "\n",
        "#print(list_of_tweets)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
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          "name": "stdout"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fLggQuxR6hjI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#  use for loop\n",
        "#for  data   in   tweet_data:\n",
        "# print(data.text)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FXG2vuLG6yEv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}