NYTimes_Test_January50.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Which place made news the most in the first week of January 2017?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of this project is to identify which Town/City/Country was mentioned most number of times in 'The New York Times' during January 1st through Jauary 7th in 2017.\n",
    "\n",
    "I use the [NYTimes Article Search API](http://developer.nytimes.com/) to extract details about article, which is then used to extract the entire articles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Importing libraries\n",
    "from nytimesarticle import articleAPI\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "api = articleAPI(nytimes_api)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Each API call returns one page of the results. Each page contains details of 10 articles.\n",
    "\n",
    "### Due to the NYTimes API call limits, we will restrict our results to 50 articles for this project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "article_details = []\n",
    "def articlesForAMonth_50(start,end):\n",
    "    '''Function that makes API calls to extract details of articles'''\n",
    "    #5 pages = 50 articles\n",
    "    for i in range(0,5):\n",
    "        article_details.append(api.search(fq = {'source':'The New York Times'}, begin_date = start, end_date = end, page=i))\n",
    "        #To avoid per second api call limit\n",
    "        time.sleep(2)\n",
    "    return article_details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extracting articles for January 2017 (date in YYYYMMDD format)\n",
    "january17_fifty = articlesForAMonth_50(20170101, 20170107)\n",
    "len(january17_fifty)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thus we have verified that details worth 5 pages of articles (or 50 articles) have been extracted successfully.\n",
    "\n",
    "### The following function, 'parse_articles' takes in the list of article details and parses it to generate a dictionary of details including:\n",
    "\n",
    "* Article ID\n",
    "* Article Headline\n",
    "* Article Type\n",
    "* Publication Date\n",
    "* Web URL\n",
    "* Locations mentioned (using Keywords)\n",
    "* Subject of the article"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def parse_articles(articles_list):\n",
    "    '''\n",
    "    This function takes in a response to the NYT api and parses\n",
    "    the articles into a list of dictionaries\n",
    "    '''\n",
    "    news = []\n",
    "    \n",
    "    for page_num in range(0,5):\n",
    "        page = articles_list[page_num]\n",
    "        for i in page['response']['docs']:\n",
    "            dic = {}\n",
    "            dic['id'] = i['_id']\n",
    "            dic['headline'] = i['headline']['main'].encode(\"utf8\")\n",
    "            dic['date'] = i['pub_date'][0:10] # cutting time of day.\n",
    "            dic['type'] = i['type_of_material']\n",
    "            dic['url'] = i['web_url']\n",
    "            # locations\n",
    "            locations = []\n",
    "            for x in range(0,len(i['keywords'])):\n",
    "                if 'glocations' in i['keywords'][x]['name']:\n",
    "                    locations.append(i['keywords'][x]['value'])\n",
    "            dic['locations'] = locations\n",
    "            # subject\n",
    "            subjects = []\n",
    "            for x in range(0,len(i['keywords'])):\n",
    "                if 'subject' in i['keywords'][x]['name']:\n",
    "                    subjects.append(i['keywords'][x]['value'])\n",
    "            dic['subjects'] = subjects   \n",
    "            news.append(dic)\n",
    "    return(news)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jan50News = parse_articles(january17_fifty)\n",
    "len(jan50News)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Thus we have verified that there have been details for all 50 articles parsed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting locations from extracted articles in a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Russia',\n",
       " 'Russia',\n",
       " 'Afghanistan',\n",
       " 'Russia',\n",
       " 'Richmond (Va)',\n",
       " 'Nashville (Tenn)',\n",
       " 'Tennessee',\n",
       " 'Washington (State)',\n",
       " 'United States',\n",
       " 'Russia',\n",
       " 'Walden Pond (Concord, Mass)',\n",
       " 'New York City',\n",
       " 'Texas',\n",
       " 'North Carolina',\n",
       " 'Fort Lauderdale (Fla)',\n",
       " 'Puerto Rico',\n",
       " 'Anchorage (Alaska)',\n",
       " 'New York State',\n",
       " 'Missouri',\n",
       " 'Ferguson (Mo)',\n",
       " 'Portugal',\n",
       " 'Alabama',\n",
       " 'Russia',\n",
       " 'Azaz (Syria)',\n",
       " 'Syria',\n",
       " 'Kilis (Turkey)',\n",
       " 'Ivory Coast',\n",
       " 'Russia',\n",
       " 'California',\n",
       " 'China',\n",
       " 'Clinton Hill (Brooklyn, NY)',\n",
       " 'China',\n",
       " 'Brazil',\n",
       " 'Cuiaba (Brazil)',\n",
       " 'China',\n",
       " 'Fort Lauderdale (Fla)']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = [jan50News[i]['locations'] for i in range(0,50)]\n",
    "places = reduce(lambda x,y : x+y, temp)\n",
    "places = [element.encode('ascii', 'ignore') for element in places]\n",
    "places"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting article contents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Importing necessary packages to parse html webpages\n",
    "import requests\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Extracting the article text in a list 'all_text'\n",
    "all_text = []\n",
    "for i in range(0,50):\n",
    "    url = jan50News[i]['url'].encode('ascii', 'ignore')\n",
    "    webpage = requests.get(url)\n",
    "    soup = BeautifulSoup(webpage.content, 'html.parser')\n",
    "    for paragraph in soup.find_all('p', class_='story-body-text'):\n",
    "        temp = (paragraph.text).encode('ascii', 'ignore')\n",
    "        all_text.append(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of paragraphs found in the extract: 1148\n"
     ]
    }
   ],
   "source": [
    "#Getting total number of paragraphs\n",
    "print 'Number of paragraphs found in the extract: ' +str(len(all_text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Saving file for future use\n",
    "myFile = open('nyTimes_Jan50articles.txt', 'w')\n",
    "for item in all_text:\n",
    "  myFile.write(\"%s\\n\" % item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Converting the list to a string for further use\n",
    "textString = ''.join(all_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#with open('nyTimes_Jan50articles.txt', 'r') as myfile:\n",
    "#    textString = myfile.read().replace('\\n', '') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### On closely observing our list of 'places' we notice a few substrings present that could affect the accuracy of results.\n",
    "\n",
    "#### Example1:\n",
    "\n",
    "'Richmond (Va)' and 'Nashville (Tenn)', though detected within the articles may not reoccur in the same format in those respective or other articles. The abbreviated states must be removed for increasing the chances of finding those cities within the collected corpus.\n",
    "\n",
    "#### Example2:\n",
    "'Walden Pond (Concord, Mass)' and 'Clinton Hill (Brooklyn, NY)', though detected precisely within the corpus, may not be ideal to plot on a map set to a global scale. It may also be easier to find more instances of 'Brooklyn' than 'Clinton Hill' within the corpus.\n",
    "Hence, the granularity needs to be removed for sake of visualizing the results.\n",
    "\n",
    "#### Example3:\n",
    "This list also contains duplicate results (eg. Russia). The duplication needs to be eliminated too.\n",
    "\n",
    "\n",
    "### Since our list of 'places' contains only 36 values, I will edit it manually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "places_edit = ['Russia', 'Afghanistan', 'Richmond', 'Nashville', 'Tennessee', 'Washington', 'United States', 'Concord',\n",
    "               'New York City', 'Texas', 'North Carolina', 'Fort Lauderdale', 'Puerto Rico', 'Anchorage', 'New York State',\n",
    "                'Missouri', 'Ferguson', 'Portugal', 'Alabama', 'Azaz', 'Syria', 'Kilis', 'Ivory Coast', 'California', \n",
    "                'China', 'Brooklyn', 'Brazil', 'Cuiaba']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We now use the variable 'textString' that contains all the articles to get a corresponding frequency count for each city in the list 'places_edit'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[108,\n",
       " 8,\n",
       " 10,\n",
       " 3,\n",
       " 2,\n",
       " 25,\n",
       " 66,\n",
       " 0,\n",
       " 8,\n",
       " 15,\n",
       " 3,\n",
       " 8,\n",
       " 6,\n",
       " 5,\n",
       " 2,\n",
       " 3,\n",
       " 5,\n",
       " 8,\n",
       " 8,\n",
       " 5,\n",
       " 14,\n",
       " 1,\n",
       " 5,\n",
       " 16,\n",
       " 31,\n",
       " 10,\n",
       " 26,\n",
       " 3]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generating list of frequencies\n",
    "places_count = [textString.count(place) for place in places_edit]\n",
    "places_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In order to plot the locations on a map, we will need to derive their latitude-longitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Import library for geocoding locations\n",
    "import geocoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[61.52401, 105.318756],\n",
       " [33.93911, 67.709953],\n",
       " [37.5407246, -77.4360481],\n",
       " [36.1626638, -86.7816016],\n",
       " [35.5174913, -86.5804473],\n",
       " [47.7510741, -120.7401386],\n",
       " [37.09024, -95.712891],\n",
       " [42.4603719, -71.3489484],\n",
       " [40.7127837, -74.0059413],\n",
       " [31.9685988, -99.9018131],\n",
       " [35.7595731, -79.01929969999999],\n",
       " [26.1224386, -80.13731740000001],\n",
       " [18.220833, -66.590149],\n",
       " [61.2180556, -149.9002778],\n",
       " [43.2994285, -74.21793260000001],\n",
       " [37.9642529, -91.8318334],\n",
       " [38.7442175, -90.3053915],\n",
       " [39.39987199999999, -8.224454],\n",
       " [32.3182314, -86.902298],\n",
       " [36.5868261, 37.0480843],\n",
       " [34.80207499999999, 38.996815],\n",
       " [36.716477, 37.114661],\n",
       " [7.539988999999999, -5.547079999999999],\n",
       " [36.778261, -119.4179324],\n",
       " [35.86166, 104.195397],\n",
       " [40.6781784, -73.9441579],\n",
       " [-14.235004, -51.92528],\n",
       " [-15.6014109, -56.0978917]]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generating list of [latitude,longitude] for each place\n",
    "lat_lon = [geocoder.google(place).latlng for place in places_edit]\n",
    "lat_lon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Separating them into 2 lists 'latitude' and 'longitude'\n",
    "latitude = [element[0] for element in lat_lon]\n",
    "longitude = [element[1] for element in lat_lon]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will now combine all the necessary lists as a Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>places</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>108</td>\n",
       "      <td>61.524010</td>\n",
       "      <td>105.318756</td>\n",
       "      <td>Russia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>33.939110</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>37.540725</td>\n",
       "      <td>-77.436048</td>\n",
       "      <td>Richmond</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>36.162664</td>\n",
       "      <td>-86.781602</td>\n",
       "      <td>Nashville</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>35.517491</td>\n",
       "      <td>-86.580447</td>\n",
       "      <td>Tennessee</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count   latitude   longitude       places\n",
       "0    108  61.524010  105.318756       Russia\n",
       "1      8  33.939110   67.709953  Afghanistan\n",
       "2     10  37.540725  -77.436048     Richmond\n",
       "3      3  36.162664  -86.781602    Nashville\n",
       "4      2  35.517491  -86.580447    Tennessee"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "results = pd.DataFrame(\n",
    "    {'places': places_edit,\n",
    "     'count': places_count,\n",
    "     'latitude': latitude,\n",
    "     'longitude': longitude\n",
    "    })\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will now use the Python package 'folium' to generate a web map for our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Importing library for plotting webmaps\n",
    "import folium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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                {
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                .setRadius(6)
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            var popup_5a3f5bb39042490bad1391ee1c0559c7 = L.popup({maxWidth: '300'});

            
                var html_69c4cceb36e44c4f863d7a38c7002532 = $('<div id="html_69c4cceb36e44c4f863d7a38c7002532" style="width: 100.0%; height: 100.0%;">California appeared 16 times in NYTimes articles</div>')[0];
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                [35.86166,104.195397],
                {
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            var popup_d2c01397abcb48dd984137ec8d050cb2 = L.popup({maxWidth: '300'});

            
                var html_49329d30ea604ad3b67d8da85d99863e = $('<div id="html_49329d30ea604ad3b67d8da85d99863e" style="width: 100.0%; height: 100.0%;">China appeared 31 times in NYTimes articles</div>')[0];
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            var circle_marker_710bf3d35cf74797af31af2281c7e2f8 = L.circleMarker(
                [40.6781784,-73.9441579],
                {
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                    weight: 2,
                    fillColor: '#ff0000',
                    fillOpacity: 0.5
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                )
                .setRadius(6)
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            var popup_2cc5704a70e74326a554b5a9a014e7e9 = L.popup({maxWidth: '300'});

            
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            var circle_marker_8370fd8c4cff4442a7a48f866b4d444a = L.circleMarker(
                [-14.235004,-51.92528],
                {
                    color: 'black',
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                    fillColor: '#ff0000',
                    fillOpacity: 0.5
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                )
                .setRadius(6)
                .addTo(map_a02a7c8d33924e19abc3ff0d3f764177);
            
    
            var popup_61cedb9d73154dea9bc4f6bbefd18f60 = L.popup({maxWidth: '300'});

            
                var html_dd394005f7cf421a9547b26849d9006b = $('<div id="html_dd394005f7cf421a9547b26849d9006b" style="width: 100.0%; height: 100.0%;">Brazil appeared 26 times in NYTimes articles</div>')[0];
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            var circle_marker_f55f0ef1de8f4dc18443b5c233a56303 = L.circleMarker(
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                {
                    color: 'black',
                    weight: 2,
                    fillColor: '#ff0000',
                    fillOpacity: 0.5
                    }
                )
                .setRadius(6)
                .addTo(map_a02a7c8d33924e19abc3ff0d3f764177);
            
    
            var popup_cfcb6c8d29f24458a0c7f81aafcebb85 = L.popup({maxWidth: '300'});

            
                var html_942e7b72af8d49258e7e4cab5689e741 = $('<div id="html_942e7b72af8d49258e7e4cab5689e741" style="width: 100.0%; height: 100.0%;">Cuiaba appeared 3 times in NYTimes articles</div>')[0];
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x6355400>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mymap = folium.Map(location=[33.642063, -13.535156], zoom_start=2)\n",
    "results.apply(lambda row:folium.CircleMarker(location=[row[\"latitude\"], row[\"longitude\"]], radius=6, popup=(str(row['places'])+' appeared '+str(row['count'])+' times in NYTimes articles'),\n",
    "                                            fill_color = '#ff0000', fill_opacity = 0.5).add_to(mymap), axis=1)\n",
    "mymap.save('mymap.html')\n",
    "mymap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The [map](https://manushim.github.io/WhichLocationMadeNews/mymap) can also be viewed here.\n",
    "\n",
    "### Click on a location to view Name and Frequency of occurence in the NYTimes during January 1-7 of 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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