Twitter_Sentiment_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the libraries\n",
    "import tweepy\n",
    "import codecs\n",
    "from aylienapiclient import textapi\n",
    "import csv, io\n",
    "import matplotlib.pyplot as plt \n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "import csv "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fill in your Twitter credentials \n",
    "access_token = ''\n",
    "access_token_secret = ''\n",
    "consumer_key = ''\n",
    "consumer_secret = ''\n",
    "\n",
    "#Set up an instance of the REST API\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_token_secret)\n",
    "api = tweepy.API(auth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#search query and store your results in a variable\n",
    "search_results = api.search(q = \"#brexit\", lang = \"en\", result_type = \"recent\", count = 100)\n",
    "\n",
    "file = codecs.open(\"brexit.txt\", \"w\", \"utf-8\")\n",
    "for result in search_results:\n",
    "    file.write(result.text)\n",
    "    file.write(\"\\n\")\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "HttpError",
     "evalue": "<HttpError 429 when requesting https://api.aylien.com/api/v1/sentiment returned \"Too Many Requests\">",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mHttpError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-c6d8fed8d36d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     17\u001b[0m                 \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m             \u001b[1;31m## Make call to AYLIEN Text API\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m             \u001b[0msentiment\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclient\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSentiment\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'text'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtweet\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m             \u001b[1;31m## Write the sentiment result into csv file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\aylienapiclient\\textapi.py\u001b[0m in \u001b[0;36mSentiment\u001b[1;34m(self, options)\u001b[0m\n\u001b[0;32m    412\u001b[0m       \u001b[1;32mraise\u001b[0m \u001b[0mMissingParameterError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'You must either provide url or text'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    413\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 414\u001b[1;33m     \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_executeRequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'sentiment'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    415\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    416\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\aylienapiclient\\textapi.py\u001b[0m in \u001b[0;36m_executeRequest\u001b[1;34m(self, endpoint, params)\u001b[0m\n\u001b[0;32m    680\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    681\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;36m300\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 682\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0mHttpError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muri\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muri\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    683\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    684\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mversion_info\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mHttpError\u001b[0m: <HttpError 429 when requesting https://api.aylien.com/api/v1/sentiment returned \"Too Many Requests\">"
     ]
    }
   ],
   "source": [
    "# Initialize a new client of AYLIEN Text API\n",
    "client = textapi.Client(\"\", \"\")\n",
    "\n",
    "with io.open('brexit_tweet_SA.csv', 'w', encoding='utf8', newline='') as csvfile:\n",
    "    csv_writer = csv.writer(csvfile)\n",
    "    csv_writer.writerow([\"Tweet\", \"Sentiment\"])\n",
    "    with io.open(\"brexit.txt\", 'r', encoding='utf8') as f:\n",
    "        for brex_tweet in f.readlines():\n",
    "            ## Remove extra spaces or newlines around the text\n",
    "            tweet = brex_tweet.strip()\n",
    "            ## Reject tweets which are empty so you don’t waste your API credits\n",
    "            if len(tweet) == 0:\n",
    "                #print('skipped')\n",
    "                continue\n",
    "            ## Make call to AYLIEN Text API\n",
    "            sentiment = client.Sentiment({'text': tweet})\n",
    "\n",
    "            ## Write the sentiment result into csv file\n",
    "            csv_writer.writerow([sentiment['text'], sentiment['polarity']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x234aae0d5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#open up your csv file with the sentiment results\n",
    "with open('brexit_tweet_SA.csv', 'r', encoding = 'utf8') as brexcsvfile:\n",
    "        # Pandas to read the “Sentiment” column,\n",
    "        df_brex = pd.read_csv(brexcsvfile)\n",
    "        sent_tweet = df_brex[\"Sentiment\"]\n",
    "\n",
    "#use Counter to count how many times each sentiment appears and save each as a variable\n",
    "        counter_var = Counter(sent_tweet)\n",
    "        positive = counter['positive']\n",
    "        negative = counter['negative']\n",
    "        neutral = counter['neutral']\n",
    "\n",
    "## declare the variables for the pie chart, using the Counter variables for “sizes”\n",
    "labels = 'Positive', 'Negative', 'Neutral'\n",
    "sizes = [positive, negative, neutral]\n",
    "colors = ['blue', 'yellow', 'black']\n",
    "text = \"brexit\"\n",
    "\n",
    "## use matplotlib to plot the chart\n",
    "plt.pie(sizes, labels = labels, colors = colors, shadow = True, startangle = 90)\n",
    "plt.title(\"Sentiment of 100 Tweets about \"+ text)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
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