yabebalFantaye · February 11, 2015 13:00
diff --git a/AfricaKeyWords.ipynb b/AfricaKeyWords.ipynb
 {
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import numpy as np\n",
      "import json\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "tweets_data_path = '../data/africa_ethiopia_tweets.txt'\n",
      "\n",
      "tweets_data = []\n",
      "tweets_file = open(tweets_data_path, \"r\")\n",
      "\n",
      "for line in tweets_file:\n",
      "    try:\n",
      "        tweet = json.loads(line)\n",
      "        tweets_data.append(tweet)\n",
      "    except:\n",
      "        continue\n",
      "        \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "def get_tweet_text(tweet):\n",
      "    #print tweet['text']\n",
      "    return tweet['text']\n",
      "\n",
      "columns = ['text', 'lang','location','followers_count','timestamp_ms'] #,'friends_count', 'country'\n",
      "index = np.arange(len(tweets_data)) # array of numbers for the number of samples\n",
      "\n",
      "tweets = pd.DataFrame(columns=columns, index = index)\n",
      "\n",
      "#print tweets_data[0]\n",
      "print tweets_data[2].keys() #['text']\n",
      "print 'saved numbers of tweets: ', len(tweets_data)\n",
      "\n",
      "\n",
      "\n",
      "#df = pd.DataFrame(columns=columns, index = index)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[u'contributors', u'truncated', u'text', u'in_reply_to_status_id', u'id', u'favorite_count', u'source', u'retweeted', u'coordinates', u'timestamp_ms', u'entities', u'in_reply_to_screen_name', u'id_str', u'retweet_count', u'in_reply_to_user_id', u'favorited', u'retweeted_status', u'user', u'geo', u'in_reply_to_user_id_str', u'possibly_sensitive', u'lang', u'created_at', u'filter_level', u'in_reply_to_status_id_str', u'place']\n",
        "saved numbers of tweets:  3053\n"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tweets['text'] = map(lambda tweet: tweet['text'], tweets_data)\n",
      "tweets['lang'] = map(lambda tweet: tweet['lang'], tweets_data)\n",
      "tweets['location'] = map(lambda tweet: tweet['user']['location'] if tweet['user']['location'] != None else u'Unknown', tweets_data)\n",
      "tweets['followers_count'] = map(lambda tweet: tweet['user']['followers_count'], tweets_data)\n",
      "tweets['timestamp_ms'] = map(lambda tweet: tweet['timestamp_ms'], tweets_data)\n",
      "\n",
      "tweets['location'].value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "                  1237\n",
        "South Africa        48\n",
        "Africa              47\n",
        "Nigeria             32\n",
        "Nairobi, Kenya      28\n",
        "Texas               26\n",
        "Nairobi             25\n",
        "Arizona             24\n",
        "Utah                24\n",
        "Lagos, Nigeria      19\n",
        "London              19\n",
        "Worldwide           18\n",
        "Cape Town           18\n",
        "Michigan            17\n",
        "Lagos               16\n",
        "...\n",
        "LasgidiS Panorama        1\n",
        "Beyond Earth             1\n",
        "Aperture Laboratories    1\n",
        "Gh. Accra                1\n",
        "Nairobi, KENYA           1\n",
        "Phalaborwa               1\n",
        "Viterbo                  1\n",
        "Africa de Sud            1\n",
        "Lakki Marwat             1\n",
        "Bulawayo, Zimbabwe       1\n",
        "Banglore                 1\n",
        "Palestine                1\n",
        "Cheshire                 1\n",
        "Ethiopia                 1\n",
        "All Around the Globe.    1\n",
        "Length: 1007, dtype: int64"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tweets_by_lang = tweets['lang'].value_counts()\n",
      "\n",
      "fig, ax = plt.subplots()\n",
      "ax.tick_params(axis='x', labelsize=15)\n",
      "ax.tick_params(axis='y', labelsize=10)\n",
      "ax.set_xlabel('Languages', fontsize=15)\n",
      "ax.set_ylabel('Number of tweets' , fontsize=15)\n",
      "ax.set_title('Top 5 languages Tweeted about Africa', fontsize=15, fontweight='bold')\n",
      "tweets_by_lang[:5].plot(ax=ax, kind='bar', color='red')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10cbe9c90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qbBSGDkBExN5fq0+UCxXKhYrYc1Gve0+VlJQgNjYWUqlUV/EQQggRsWrHNJYv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\nqjQIIYQI9v8AeJjVdX/+U1QAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10cf27e50>"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tweets_by_country = tweets['location'].value_counts()\n",
      "\n",
      "fig, ax = plt.subplots()\n",
      "ax.tick_params(axis='x', labelsize=15)\n",
      "ax.tick_params(axis='y', labelsize=10)\n",
      "ax.set_xlabel('Countries', fontsize=15)\n",
      "ax.set_ylabel('Number of tweets' , fontsize=15)\n",
      "ax.set_title('Top 5 locations', fontsize=15, fontweight='bold')\n",
      "tweets_by_country[1:8].plot(ax=ax, kind='bar', color='blue')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10d01b050>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vw49BCCGkrjQaAIqLi1Xe9KWwsBANGjTQuEMLCwssWrQIf/31F06ePImYmBj8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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x104824e10>"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'''\n",
      "Tweet Mining:\n",
      "---------------\n",
      "In this section we will mine the tweets to learn about interesting topics \n",
      "=======================================================\n",
      "'''\n",
      "\n",
      "import re #regtxt parser\n",
      "\n",
      "#define topics \n",
      "keytopic=['power','safari','happy','freedom','prison']\n",
      "\n",
      "#Create a function called word_in_text(word, text). \n",
      "#to return True if a word is found in text, otherwise it returns False.\n",
      "def word_in_text(word, text):\n",
      "    word = word.lower()\n",
      "    text = text.lower()\n",
      "    match = re.search(word, text)        \n",
      "    if match:\n",
      "        #print text\n",
      "        return True        \n",
      "    return False\n",
      "\n",
      "#add columns in the DataFrame to be True if a topic is found in the tweet else False\n",
      "for itopic in range(len(keytopic)):\n",
      "    tweets[keytopic[itopic]] = tweets['text'].apply(lambda tweet: word_in_text(keytopic[itopic], tweet))\n",
      "\n",
      "#calculate the number of tweets for each  topic\n",
      "tweets_by_topic = [tweets[keytopic[itopic]].value_counts()[True] for itopic in range(len(keytopic))]\n",
      "\n",
      "#=========================================\n",
      "#================ show plots============\n",
      "#=========================================\n",
      "#show a bar plot which illustrates the relative popularity the topics we consider\n",
      "x_pos = list(range(len(keytopic)))\n",
      "width = 0.8\n",
      "fig, ax = plt.subplots()\n",
      "plt.bar(x_pos, tweets_by_topic, width, alpha=1, color='g')\n",
      "\n",
      "# Setting axis labels and ticks\n",
      "ax.set_ylabel('Number of tweets', fontsize=15)\n",
      "ax.set_title('Ranking: By topic', fontsize=10, fontweight='bold')\n",
      "ax.set_xticks([p + 0.4 * width for p in x_pos])\n",
      "ax.set_xticklabels(keytopic)\n",
      "plt.grid()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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f5alsTM8cHOuVMRcy5sKAyvoLk28IU6NGDbi4uMDNze2Rdjhu3Dh4enrCx8dH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\nx48jJCQELVq0wPfff2/dZJS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       "text": [
        "<matplotlib.figure.Figure at 0x10c611550>"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'''\n",
      "Targeting relevant tweets with location filter\n",
      "***Not yet working as desired\n",
      "--------------------------------------------------------\n",
      "'''\n",
      "\n",
      "NewTopic=['crisis ','solar','africa']\n",
      "\n",
      "for itopic in range(len(NewTopic)):\n",
      "    tweets[NewTopic[itopic]] = tweets['text'].apply(lambda tweet: word_in_text(NewTopic[itopic], tweet))\n",
      "\n",
      "\n",
      "#find location where the NewTopic is tweeted\n",
      "tweets_newtopic = [tweets[tweets[NewTopic[itopic]] == True]['timestamp_ms'].value_counts()[True] for itopic in range(len(NewTopic))]\n",
      "\n",
      "#=========================================\n",
      "#================ show plots============\n",
      "#=========================================\n",
      "#show a bar plot which illustrates the relative popularity the topics we consider\n",
      "x_pos = list(range(len(NewTopic)))\n",
      "width = 0.8\n",
      "fig, ax = plt.subplots()\n",
      "plt.bar(x_pos, tweets_newtopic, width, alpha=1, color='g')\n",
      "\n",
      "# Setting axis labels and ticks\n",
      "ax.set_ylabel('Number of tweets', fontsize=15)\n",
      "ax.set_title('Ranking: By topic', fontsize=10, fontweight='bold')\n",
      "ax.set_xticks([p + 0.4 * width for p in x_pos])\n",
      "ax.set_xticklabels(NewTopic)\n",
      "plt.grid()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10cf17ed0>"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
 }
No results found