PA_Sentiment_Analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "PA: Sentiment Analysis.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "pSDWsnqOim1N"
      },
      "source": [
        "#Description: A Sentiment Analysis Program"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kFiFxN2VXJxB"
      },
      "source": [
        "import tweepy\r\n",
        "from textblob import TextBlob\r\n",
        "#from wordcloud import WordCloud\r\n",
        "#import pandas as pd\r\n",
        "#import numpy as np\r\n",
        "#import matplotlib.pyplot as plt\r\n",
        "#plt.style.use('fivethirtyeight')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "4UGTDx74jNyw",
        "outputId": "0642a2bf-ed1f-4cdd-f19c-6d7d5fd3d05a"
      },
      "source": [
        "#loading data\r\n",
        "from google.colab import files\r\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-434971c7-1c51-49c5-8604-2b1c2c5b23f8\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-434971c7-1c51-49c5-8604-2b1c2c5b23f8\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving keys.txt to keys.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BydiKuN-jNtF"
      },
      "source": [
        "#get data\r\n",
        "mykeys = open('keys.txt','r').read().splitlines()"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yb9iruIbjNgR"
      },
      "source": [
        "# Twitter API Credentials\r\n",
        "api_key = mykeys[0].split()[-1]\r\n",
        "api_key_secret = mykeys[1].split()[-1]\r\n",
        "access_token = mykeys[2].split()[-1]\r\n",
        "access_token_secret = mykeys[3].split()[-1]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ncDvjWwQJH7s"
      },
      "source": [
        "# Connecting to the API\r\n",
        "auth_handler = tweepy.OAuthHandler(consumer_key = api_key, consumer_secret = api_key_secret)\r\n",
        "#set access to the access token\r\n",
        "auth_handler.set_access_token(access_token, access_token_secret)\r\n",
        "# creating the API\r\n",
        "api = tweepy.API(auth_handler)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ftvjKpzVKdON",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "05374fb5-acf5-4f34-d219-d86f538a823a"
      },
      "source": [
        "#search_term = 'stocks'\r\n",
        "search_term = input('\\nEnter the word to search: ')\r\n",
        "tweet_amount = 200\r\n",
        "polarity = 0 #indicates if the text is positive or negative\r\n",
        "positive, negative, neutral = 0, 0, 0"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Enter the word to search: drought\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jl1Zmlh8K2cb"
      },
      "source": [
        "tweets = tweepy.Cursor(api.search, q = search_term, lang = 'en').items(tweet_amount)\r\n",
        "for tweet in tweets:\r\n",
        "  final_text = tweet.text.replace('RT', '')\r\n",
        "  if final_text.startswith(' @'):\r\n",
        "    position = final_text.index(':')\r\n",
        "    final_text = final_text[position+2:]\r\n",
        "  if final_text.startswith('@'):\r\n",
        "    position = final_text.index(' ')\r\n",
        "    final_text = final_text[position+2:]\r\n",
        "  #print(final_text)\r\n",
        "  analysis = TextBlob(final_text)\r\n",
        "  polarity += analysis.polarity\r\n",
        "  tweet_polarity = analysis.polarity\r\n",
        "  if tweet_polarity > 0:\r\n",
        "    positive += 1\r\n",
        "  elif tweet_polarity < 0: \r\n",
        "    negative += 1\r\n",
        "  else:\r\n",
        "    neutral += 1\r\n",
        "    polarity += tweet_polarity"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l1JeIwUq7qQ7",
        "outputId": "712d11c5-a9af-42ed-e3c2-75940c978469"
      },
      "source": [
        "print('overall polarity ',polarity)\r\n",
        "print('positive pol ', positive)\r\n",
        "print('negative pol ', negative)\r\n",
        "print('neutral pol ', neutral)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "overall polarity  -9.197255383505388\n",
            "positive pol  44\n",
            "negative pol  69\n",
            "neutral pol  87\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I4MRf3eQ8Wys"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6j95iMuK8Wwb"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}