Australian News Topics and National Coversation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FOCUS OF ANALYSIS\n",
    "\n",
    "What were the top Australian news topics over the last decade? ii) what can these say about the national conversation?\n",
    "\n",
    "DESCRIPTION\n",
    "\n",
    "Knowing and analysing the top australian new headlines over the period of 2010 to 2019, would be helpful in understanding the hot news topics which dominated the national conversation over a certain period. Stakeholders including government and any business sectors can utilise this analysis to understand how other business competitors dominated the national conversation at a particular time. Other news channels may also be interested in this analysis.\n",
    "Different business sectors to understand, what is important to australians at different times, What are the news, which created awareness among people (national conversation)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DATA SOURCES\n",
    "\n",
    "The dataset used in this analysis is the one which was derived from kaggle containing ABC national news headlines from the year 2003 to 2019. (https://www.kaggle.com/therohk/million-headlines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nltk in c:\\users\\abina\\anaconda3\\lib\\site-packages (3.4.5)\n",
      "Requirement already satisfied: six in c:\\users\\abina\\anaconda3\\lib\\site-packages (from nltk) (1.12.0)\n"
     ]
    }
   ],
   "source": [
    "#import libraries\n",
    "import pandas as pd    # used for data manipulation and data analysis\n",
    "import numpy as np\n",
    "import re              #used for regular expression\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt    # used for visualisation\n",
    "import sys\n",
    "!{sys.executable} -m pip install nltk\n",
    "from nltk.corpus import stopwords  # used for stopwords removal \n",
    "\n",
    "\n",
    "#Code to load data\n",
    "file_path = \"abcnews-date-text.csv\"\n",
    "data3 = pd.read_csv(file_path,parse_dates=[0],infer_datetime_format=True)\n",
    "\n",
    "data3['publish_date'] = pd.to_datetime(data3.publish_date)\n",
    "data3['Year'] = data3.publish_date.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>publish_date</th>\n",
       "      <th>headline_text</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2003-02-19</td>\n",
       "      <td>aba decides against community broadcasting lic...</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2003-02-19</td>\n",
       "      <td>act fire witnesses must be aware of defamation</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2003-02-19</td>\n",
       "      <td>a g calls for infrastructure protection summit</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2003-02-19</td>\n",
       "      <td>air nz staff in aust strike for pay rise</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2003-02-19</td>\n",
       "      <td>air nz strike to affect australian travellers</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1186013</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>vision of flames approaching corryong in victoria</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1186014</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>wa police and government backflip on drug amne...</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1186015</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>we have fears for their safety: victorian premier</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1186016</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>when do the 20s start</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1186017</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>yarraville shooting woman dead man critically ...</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1186018 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        publish_date                                      headline_text  Year\n",
       "0         2003-02-19  aba decides against community broadcasting lic...  2003\n",
       "1         2003-02-19     act fire witnesses must be aware of defamation  2003\n",
       "2         2003-02-19     a g calls for infrastructure protection summit  2003\n",
       "3         2003-02-19           air nz staff in aust strike for pay rise  2003\n",
       "4         2003-02-19      air nz strike to affect australian travellers  2003\n",
       "...              ...                                                ...   ...\n",
       "1186013   2019-12-31  vision of flames approaching corryong in victoria  2019\n",
       "1186014   2019-12-31  wa police and government backflip on drug amne...  2019\n",
       "1186015   2019-12-31  we have fears for their safety: victorian premier  2019\n",
       "1186016   2019-12-31                              when do the 20s start  2019\n",
       "1186017   2019-12-31  yarraville shooting woman dead man critically ...  2019\n",
       "\n",
       "[1186018 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Viewing dataset\n",
    "data3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ANALYSIS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1186018 entries, 0 to 1186017\n",
      "Data columns (total 3 columns):\n",
      "publish_date     1186018 non-null datetime64[ns]\n",
      "headline_text    1186018 non-null object\n",
      "Year             1186018 non-null int64\n",
      "dtypes: datetime64[ns](1), int64(1), object(1)\n",
      "memory usage: 27.1+ MB\n"
     ]
    }
   ],
   "source": [
    "#Knowing data \n",
    "data3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [
    {
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      "text/plain": [
       "       publish_date                            headline_text  Year\n",
       "510305   2010-01-01             12m degas stolen from museum  2010\n",
       "510306   2010-01-01           17 die in bangladesh bus crash  2010\n",
       "510307   2010-01-01     2010 to be drier after very wet 2009  2010\n",
       "510308   2010-01-01     37 catholic workers killed worldwide  2010\n",
       "510309   2010-01-01  600 hurt in philippine new year revelry  2010"
      ]
     },
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     "metadata": {},
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    }
   ],
   "source": [
    "#Trimming data to last decade\n",
    "reduced_data = data3[510305:]                   \n",
    "reduced_data.head()"
   ]
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   "execution_count": 5,
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      "C:\\Users\\abina\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
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       "       publish_date                                    headline_text  Year\n",
       "510305   2010-01-01               [12m, degas, stolen, from, museum]  2010\n",
       "510306   2010-01-01            [17, die, in, bangladesh, bus, crash]  2010\n",
       "510307   2010-01-01    [2010, to, be, drier, after, very, wet, 2009]  2010\n",
       "510308   2010-01-01       [37, catholic, workers, killed, worldwide]  2010\n",
       "510309   2010-01-01  [600, hurt, in, philippine, new, year, revelry]  2010"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "#Tokenization\n",
    "def tokenize(text):\n",
    "    tokens=re.split('\\W+',text)\n",
    "    return tokens\n",
    "reduced_data['headline_text'] = reduced_data.headline_text.apply(lambda x: tokenize(x.lower()))\n",
    "reduced_data.head()"
   ]
  },
  {
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   "execution_count": 6,
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   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "C:\\Users\\abina\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n"
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       "      <td>2010</td>\n",
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      "text/plain": [
       "       publish_date                                    headline_text  Year  \\\n",
       "510305   2010-01-01               [12m, degas, stolen, from, museum]  2010   \n",
       "510306   2010-01-01            [17, die, in, bangladesh, bus, crash]  2010   \n",
       "510307   2010-01-01    [2010, to, be, drier, after, very, wet, 2009]  2010   \n",
       "510308   2010-01-01       [37, catholic, workers, killed, worldwide]  2010   \n",
       "510309   2010-01-01  [600, hurt, in, philippine, new, year, revelry]  2010   \n",
       "\n",
       "                     stopword_removed_headline_text  \n",
       "510305                 [12m, degas, stolen, museum]  \n",
       "510306            [17, die, bangladesh, bus, crash]  \n",
       "510307                     [2010, drier, wet, 2009]  \n",
       "510308   [37, catholic, workers, killed, worldwide]  \n",
       "510309  [600, hurt, philippine, new, year, revelry]  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#stopwords\n",
    "stopwords = stopwords.words('english')\n",
    "\n",
    "def remove_stopwords(text_tokenized):\n",
    "    sw_removed_headline_text = [word for word in text_tokenized if word not in stopwords]\n",
    "    return sw_removed_headline_text\n",
    "reduced_data['stopword_removed_headline_text']= reduced_data['headline_text'].apply(lambda x:remove_stopwords(x))\n",
    "reduced_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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       "      <td>10</td>\n",
       "      <td>fire</td>\n",
       "      <td>8249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>government</td>\n",
       "      <td>7802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>sydney</td>\n",
       "      <td>7736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>wa</td>\n",
       "      <td>7631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>council</td>\n",
       "      <td>7357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>rural</td>\n",
       "      <td>6842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>day</td>\n",
       "      <td>6688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>death</td>\n",
       "      <td>6670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>charged</td>\n",
       "      <td>6291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>murder</td>\n",
       "      <td>6186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Character  char_count\n",
       "0       police       18531\n",
       "1          new       17915\n",
       "2          man       17112\n",
       "3         says       13682\n",
       "4    interview       12435\n",
       "5    australia       11188\n",
       "6   australian        9799\n",
       "7        court        9635\n",
       "8          nsw        9263\n",
       "9           us        8428\n",
       "10        fire        8249\n",
       "11  government        7802\n",
       "12      sydney        7736\n",
       "13          wa        7631\n",
       "14     council        7357\n",
       "15       rural        6842\n",
       "16         day        6688\n",
       "17       death        6670\n",
       "18     charged        6291\n",
       "19      murder        6186"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identifying the most common words (top 20) last decade\n",
    "headline_list = reduced_data['stopword_removed_headline_text']\n",
    "headline_list.values.tolist()\n",
    "headline_list.head()\n",
    "\n",
    "from itertools import chain\n",
    "headline_list = list(chain.from_iterable(headline_list))\n",
    "\n",
    "from collections import Counter\n",
    "count = Counter(headline_list).most_common(20)\n",
    "character = []\n",
    "char_values = []\n",
    "for x in count:\n",
    "    character.append(x[0])\n",
    "    char_values.append(x[1])\n",
    "mapping = pd.DataFrame(character,columns =['Character'])\n",
    "mapping[\"char_count\"] = char_values\n",
    "mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Character</th>\n",
       "      <th>char_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>police</td>\n",
       "      <td>1043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>australia</td>\n",
       "      <td>953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>man</td>\n",
       "      <td>835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>new</td>\n",
       "      <td>823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>australian</td>\n",
       "      <td>782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>election</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>trump</td>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>says</td>\n",
       "      <td>709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>us</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>sydney</td>\n",
       "      <td>651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Character  char_count\n",
       "0      police        1043\n",
       "1   australia         953\n",
       "2         man         835\n",
       "3         new         823\n",
       "4  australian         782\n",
       "5    election         713\n",
       "6       trump         710\n",
       "7        says         709\n",
       "8          us         678\n",
       "9      sydney         651"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identifying most common words in the news headline for the year 2019\n",
    "year2019_news = reduced_data.loc[reduced_data['Year'] == 2019]     \n",
    "\n",
    "year2019_list = year2019_news['stopword_removed_headline_text']\n",
    "year2019_list.values.tolist()\n",
    "year2019_list.head()\n",
    "\n",
    "from itertools import chain\n",
    "year2019_list = list(chain.from_iterable(year2019_list))\n",
    "\n",
    "from collections import Counter\n",
    "year2019_count = Counter(year2019_list).most_common(10)\n",
    "character = []\n",
    "char_values = []\n",
    "for x in year2019_count:\n",
    "    character.append(x[0])\n",
    "    char_values.append(x[1])\n",
    "year2019mapping = pd.DataFrame(character,columns =['Character'])\n",
    "year2019mapping[\"char_count\"] = char_values\n",
    "year2019mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Character</th>\n",
       "      <th>char_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>says</td>\n",
       "      <td>1142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>trump</td>\n",
       "      <td>1099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>police</td>\n",
       "      <td>1083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>australia</td>\n",
       "      <td>1043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>new</td>\n",
       "      <td>992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>australian</td>\n",
       "      <td>968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>man</td>\n",
       "      <td>862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>us</td>\n",
       "      <td>813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "      <td>631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>sydney</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Character  char_count\n",
       "0        says        1142\n",
       "1       trump        1099\n",
       "2      police        1083\n",
       "3   australia        1043\n",
       "4         new         992\n",
       "5  australian         968\n",
       "6         man         862\n",
       "7          us         813\n",
       "8       world         631\n",
       "9      sydney         624"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identifying most common words in the news headline for the year 2018\n",
    "year2018_news = reduced_data.loc[reduced_data['Year'] == 2018]\n",
    "\n",
    "year2018_list = year2018_news['stopword_removed_headline_text']\n",
    "year2018_list.values.tolist()\n",
    "year2018_list.head()\n",
    "\n",
    "from itertools import chain\n",
    "year2018_list = list(chain.from_iterable(year2018_list))\n",
    "\n",
    "from collections import Counter\n",
    "year2018_count = Counter(year2018_list).most_common(10)\n",
    "character = []\n",
    "char_values = []\n",
    "for x in year2018_count:\n",
    "    character.append(x[0])\n",
    "    char_values.append(x[1])\n",
    "year2018mapping = pd.DataFrame(character,columns =['Character'])\n",
    "year2018mapping[\"char_count\"] = char_values\n",
    "year2018mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Character</th>\n",
       "      <th>char_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>says</td>\n",
       "      <td>1432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>new</td>\n",
       "      <td>1366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>trump</td>\n",
       "      <td>1359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>australia</td>\n",
       "      <td>1304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>police</td>\n",
       "      <td>1206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>australian</td>\n",
       "      <td>988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>wa</td>\n",
       "      <td>978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>man</td>\n",
       "      <td>961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>us</td>\n",
       "      <td>851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>nsw</td>\n",
       "      <td>760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Character  char_count\n",
       "0        says        1432\n",
       "1         new        1366\n",
       "2       trump        1359\n",
       "3   australia        1304\n",
       "4      police        1206\n",
       "5  australian         988\n",
       "6          wa         978\n",
       "7         man         961\n",
       "8          us         851\n",
       "9         nsw         760"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identifying most common words in the news headline for the year 2017\n",
    "year2017_news = reduced_data.loc[reduced_data['Year'] == 2017]\n",
    "\n",
    "year2017_list = year2017_news['stopword_removed_headline_text']\n",
    "year2017_list.values.tolist()\n",
    "year2017_list.head()\n",
    "\n",
    "from itertools import chain\n",
    "year2017_list = list(chain.from_iterable(year2017_list))\n",
    "\n",
    "from collections import Counter\n",
    "year2017_count = Counter(year2017_list).most_common(10)\n",
    "character = []\n",
    "char_values = []\n",
    "for x in year2017_count:\n",
    "    character.append(x[0])\n",
    "    char_values.append(x[1])\n",
    "year2017mapping = pd.DataFrame(character,columns =['Character'])\n",
    "year2017mapping[\"char_count\"] = char_values\n",
    "year2017mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Character</th>\n",
       "      <th>char_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>new</td>\n",
       "      <td>1824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>man</td>\n",
       "      <td>1816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>says</td>\n",
       "      <td>1724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>police</td>\n",
       "      <td>1690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>australia</td>\n",
       "      <td>1464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>australian</td>\n",
       "      <td>1260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>wa</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2016</td>\n",
       "      <td>1189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>election</td>\n",
       "      <td>1090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>nsw</td>\n",
       "      <td>1066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Character  char_count\n",
       "0         new        1824\n",
       "1         man        1816\n",
       "2        says        1724\n",
       "3      police        1690\n",
       "4   australia        1464\n",
       "5  australian        1260\n",
       "6          wa        1200\n",
       "7        2016        1189\n",
       "8    election        1090\n",
       "9         nsw        1066"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identifying most common words in the news headline for the year 2016\n",
    "year2016_news = reduced_data.loc[reduced_data['Year'] == 2016]\n",
    "\n",
    "year2016_list = year2016_news['stopword_removed_headline_text']\n",
    "year2016_list.values.tolist()\n",
    "year2016_list.head()\n",
    "\n",
    "from itertools import chain\n",
    "year2016_list = list(chain.from_iterable(year2016_list))\n",
    "\n",
    "from collections import Counter\n",
    "year2016_count = Counter(year2016_list).most_common(10)\n",
    "character = []\n",
    "char_values = []\n",
    "for x in year2016_count:\n",
    "    character.append(x[0])\n",
    "    char_values.append(x[1])\n",
    "year2016mapping = pd.DataFrame(character,columns =['Character'])\n",
    "year2016mapping[\"char_count\"] = char_values\n",
    "year2016mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Character</th>\n",
       "      <th>char_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>new</td>\n",
       "      <td>2509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>police</td>\n",
       "      <td>2173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>man</td>\n",
       "      <td>2143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>says</td>\n",
       "      <td>2089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>australia</td>\n",
       "      <td>1678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>nsw</td>\n",
       "      <td>1525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>australian</td>\n",
       "      <td>1510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>wa</td>\n",
       "      <td>1466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>country</td>\n",
       "      <td>1354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Character  char_count\n",
       "0         new        2509\n",
       "1      police        2173\n",
       "2         man        2143\n",
       "3        says        2089\n",
       "4   australia        1678\n",
       "5        2015        1659\n",
       "6         nsw        1525\n",
       "7  australian        1510\n",
       "8          wa        1466\n",
       "9     country        1354"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identifying most common words in the news headline for the year 2015\n",
    "year2015_news = reduced_data.loc[reduced_data['Year'] == 2015]\n",
    "\n",
    "year2015_list = year2015_news['stopword_removed_headline_text']\n",
    "year2015_list.values.tolist()\n",
    "year2015_list.head()\n",
    "\n",
    "from itertools import chain\n",
    "year2015_list = list(chain.from_iterable(year2015_list))\n",
    "\n",
    "from collections import Counter\n",
    "year2015_count = Counter(year2015_list).most_common(10)\n",
    "character = []\n",
    "char_values = []\n",
    "for x in year2015_count:\n",
    "    character.append(x[0])\n",
    "    char_values.append(x[1])\n",
    "year2015mapping = pd.DataFrame(character,columns =['Character'])\n",
    "year2015mapping[\"char_count\"] = char_values\n",
    "year2015mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Character</th>\n",
       "      <th>char_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>new</td>\n",
       "      <td>2405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>police</td>\n",
       "      <td>2134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>man</td>\n",
       "      <td>2029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>says</td>\n",
       "      <td>1870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>interview</td>\n",
       "      <td>1823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>rural</td>\n",
       "      <td>1745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>nsw</td>\n",
       "      <td>1519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2014</td>\n",
       "      <td>1490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>australia</td>\n",
       "      <td>1486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>australian</td>\n",
       "      <td>1344</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Character  char_count\n",
       "0         new        2405\n",
       "1      police        2134\n",
       "2         man        2029\n",
       "3        says        1870\n",
       "4   interview        1823\n",
       "5       rural        1745\n",
       "6         nsw        1519\n",
       "7        2014        1490\n",
       "8   australia        1486\n",
       "9  australian        1344"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identifying most common words in the news headline for the year 2014\n",
    "year2014_news = reduced_data.loc[reduced_data['Year'] == 2014]\n",
    "\n",
    "year2014_list = year2014_news['stopword_removed_headline_text']\n",
    "year2014_list.values.tolist()\n",
    "year2014_list.head()\n",
    "\n",
    "from itertools import chain\n",
    "year2014_list = list(chain.from_iterable(year2014_list))\n",
    "\n",
    "from collections import Counter\n",
    "year2014_count = Counter(year2014_list).most_common(10)\n",
    "character = []\n",
    "char_values = []\n",
    "for x in year2014_count:\n",
    "    character.append(x[0])\n",
    "    char_values.append(x[1])\n",
    "year2014mapping = pd.DataFrame(character,columns =['Character'])\n",
    "year2014mapping[\"char_count\"] = char_values\n",
    "year2014mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Character</th>\n",
       "      <th>char_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>new</td>\n",
       "      <td>2533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>police</td>\n",
       "      <td>2444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>man</td>\n",
       "      <td>2267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>interview</td>\n",
       "      <td>2167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>says</td>\n",
       "      <td>1618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>rural</td>\n",
       "      <td>1468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>court</td>\n",
       "      <td>1361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>fire</td>\n",
       "      <td>1356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>nsw</td>\n",
       "      <td>1308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>council</td>\n",
       "      <td>1260</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Character  char_count\n",
       "0        new        2533\n",
       "1     police        2444\n",
       "2        man        2267\n",
       "3  interview        2167\n",
       "4       says        1618\n",
       "5      rural        1468\n",
       "6      court        1361\n",
       "7       fire        1356\n",
       "8        nsw        1308\n",
       "9    council        1260"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identifying most common words in the news headline for the year 2013\n",
    "year2013_news = reduced_data.loc[reduced_data['Year'] == 2013]\n",
    "\n",
    "year2013_list = year2013_news['stopword_removed_headline_text']\n",
    "year2013_list.values.tolist()\n",
    "year2013_list.head()\n",
    "\n",
    "from itertools import chain\n",
    "year2013_list = list(chain.from_iterable(year2013_list))\n",
    "\n",
    "from collections import Counter\n",
    "year2013_count = Counter(year2013_list).most_common(10)\n",
    "character = []\n",
    "char_values = []\n",
    "for x in year2013_count:\n",
    "    character.append(x[0])\n",
    "    char_values.append(x[1])\n",
    "year2013mapping = pd.DataFrame(character,columns =['Character'])\n",
    "year2013mapping[\"char_count\"] = char_values\n",
    "year2013mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Character</th>\n",
       "      <th>char_count</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>interview</td>\n",
       "      <td>2438</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>police</td>\n",
       "      <td>2249</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>new</td>\n",
       "      <td>2042</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>man</td>\n",
       "      <td>1984</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>abc</td>\n",
       "      <td>1701</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>court</td>\n",
       "      <td>1238</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>says</td>\n",
       "      <td>1236</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>fire</td>\n",
       "      <td>1111</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>council</td>\n",
       "      <td>1058</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>market</td>\n",
       "      <td>972</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Character  char_count  Year\n",
       "0  interview        2438  2012\n",
       "1     police        2249  2012\n",
       "2        new        2042  2012\n",
       "3        man        1984  2012\n",
       "4        abc        1701  2012\n",
       "5      court        1238  2012\n",
       "6       says        1236  2012\n",
       "7       fire        1111  2012\n",
       "8    council        1058  2012\n",
       "9     market         972  2012"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identifying most common words in the news headline for the year 2012\n",
    "year2012_news = reduced_data.loc[reduced_data['Year'] == 2012]\n",
    "\n",
    "year2012_list = year2012_news['stopword_removed_headline_text']\n",
    "year2012_list.values.tolist()\n",
    "year2012_list.head()\n",
    "\n",
    "from itertools import chain\n",
    "year2012_list = list(chain.from_iterable(year2012_list))\n",
    "\n",
    "from collections import Counter\n",
    "year2012_count = Counter(year2012_list).most_common(10)\n",
    "character = []\n",
    "char_values = []\n",
    "for x in year2012_count:\n",
    "    character.append(x[0])\n",
    "    char_values.append(x[1])\n",
    "year2012mapping = pd.DataFrame(character,columns =['Character'])\n",
    "year2012mapping[\"char_count\"] = char_values\n",
    "year2012mapping[\"Year\"] = 2012\n",
    "year2012mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Character</th>\n",
       "      <th>char_count</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>police</td>\n",
       "      <td>2104</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>man</td>\n",
       "      <td>1968</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>interview</td>\n",
       "      <td>1776</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>new</td>\n",
       "      <td>1762</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>flood</td>\n",
       "      <td>1280</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>court</td>\n",
       "      <td>1032</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>call</td>\n",
       "      <td>968</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>council</td>\n",
       "      <td>951</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>abc</td>\n",
       "      <td>945</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>says</td>\n",
       "      <td>930</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Character  char_count  Year\n",
       "0     police        2104  2011\n",
       "1        man        1968  2011\n",
       "2  interview        1776  2011\n",
       "3        new        1762  2011\n",
       "4      flood        1280  2011\n",
       "5      court        1032  2011\n",
       "6       call         968  2011\n",
       "7    council         951  2011\n",
       "8        abc         945  2011\n",
       "9       says         930  2011"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identifying most common words in the news headline for the year 2011\n",
    "year2011_news = reduced_data.loc[reduced_data['Year'] == 2011]\n",
    "\n",
    "year2011_list = year2011_news['stopword_removed_headline_text']\n",
    "year2011_list.values.tolist()\n",
    "year2011_list.head()\n",
    "\n",
    "from itertools import chain\n",
    "year2011_list = list(chain.from_iterable(year2011_list))\n",
    "\n",
    "from collections import Counter\n",
    "year2011_count = Counter(year2011_list).most_common(10)\n",
    "character = []\n",
    "char_values = []\n",
    "for x in year2011_count:\n",
    "    character.append(x[0])\n",
    "    char_values.append(x[1])\n",
    "year2011mapping = pd.DataFrame(character,columns =['Character'])\n",
    "year2011mapping[\"char_count\"] = char_values\n",
    "year2011mapping[\"Year\"] = 2011\n",
    "year2011mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Character</th>\n",
       "      <th>char_count</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>interview</td>\n",
       "      <td>2807</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>police</td>\n",
       "      <td>2405</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>man</td>\n",
       "      <td>2247</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>new</td>\n",
       "      <td>1659</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>court</td>\n",
       "      <td>1168</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>council</td>\n",
       "      <td>1081</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>says</td>\n",
       "      <td>932</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>water</td>\n",
       "      <td>897</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>crash</td>\n",
       "      <td>874</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>fire</td>\n",
       "      <td>860</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Character  char_count  Year\n",
       "0  interview        2807  2010\n",
       "1     police        2405  2010\n",
       "2        man        2247  2010\n",
       "3        new        1659  2010\n",
       "4      court        1168  2010\n",
       "5    council        1081  2010\n",
       "6       says         932  2010\n",
       "7      water         897  2010\n",
       "8      crash         874  2010\n",
       "9       fire         860  2010"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identifying most common words in the news headline for the year 2010\n",
    "year2010_news = reduced_data.loc[reduced_data['Year'] == 2010]\n",
    "\n",
    "year2010_list = year2010_news['stopword_removed_headline_text']\n",
    "year2010_list.values.tolist()\n",
    "year2010_list.head()\n",
    "\n",
    "from itertools import chain\n",
    "year2010_list = list(chain.from_iterable(year2010_list))\n",
    "\n",
    "from collections import Counter\n",
    "year2010_count = Counter(year2010_list).most_common(10)\n",
    "character = []\n",
    "char_values = []\n",
    "for x in year2010_count:\n",
    "    character.append(x[0])\n",
    "    char_values.append(x[1])\n",
    "year2010mapping = pd.DataFrame(character,columns =['Character'])\n",
    "year2010mapping[\"char_count\"] = char_values\n",
    "year2010mapping[\"Year\"] = 2010\n",
    "year2010mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Character</th>\n",
       "      <th>char_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>police</td>\n",
       "      <td>11336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>interview</td>\n",
       "      <td>11011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>man</td>\n",
       "      <td>10495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>new</td>\n",
       "      <td>10401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>says</td>\n",
       "      <td>6586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>court</td>\n",
       "      <td>5928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>council</td>\n",
       "      <td>5454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>fire</td>\n",
       "      <td>5145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>nsw</td>\n",
       "      <td>4765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>australia</td>\n",
       "      <td>4746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Character  char_count\n",
       "0     police       11336\n",
       "1  interview       11011\n",
       "2        man       10495\n",
       "3        new       10401\n",
       "4       says        6586\n",
       "5      court        5928\n",
       "6    council        5454\n",
       "7       fire        5145\n",
       "8        nsw        4765\n",
       "9  australia        4746"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Year 2010-2014 analysis\n",
    "interviews = (reduced_data['Year'] >= 2010) & (reduced_data['Year'] <= 2014)\n",
    "interviews = reduced_data.loc[interviews]\n",
    "interviews\n",
    "\n",
    "interviews_list = interviews['stopword_removed_headline_text']\n",
    "interviews_list.values.tolist()\n",
    "\n",
    "from itertools import chain\n",
    "interviews_list = list(chain.from_iterable(interviews_list))\n",
    "\n",
    "from collections import Counter\n",
    "interview_count = Counter(interviews_list).most_common(10)\n",
    "character = []\n",
    "char_values = []\n",
    "for x in interview_count:\n",
    "    character.append(x[0])\n",
    "    char_values.append(x[1])\n",
    "interview_mapping = pd.DataFrame(character,columns =['Character'])\n",
    "interview_mapping[\"char_count\"] = char_values\n",
    "interview_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Character</th>\n",
       "      <th>char_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>new</td>\n",
       "      <td>7514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>police</td>\n",
       "      <td>7195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>says</td>\n",
       "      <td>7096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>man</td>\n",
       "      <td>6617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>australia</td>\n",
       "      <td>6442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>australian</td>\n",
       "      <td>5508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>nsw</td>\n",
       "      <td>4498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>wa</td>\n",
       "      <td>4483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>us</td>\n",
       "      <td>4375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>trump</td>\n",
       "      <td>3991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Character  char_count\n",
       "0         new        7514\n",
       "1      police        7195\n",
       "2        says        7096\n",
       "3         man        6617\n",
       "4   australia        6442\n",
       "5  australian        5508\n",
       "6         nsw        4498\n",
       "7          wa        4483\n",
       "8          us        4375\n",
       "9       trump        3991"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Year 2015-2019 analysis\n",
    "us_domination = (reduced_data['Year'] >= 2015) & (reduced_data['Year'] <= 2019)\n",
    "us_domination = reduced_data.loc[us_domination]\n",
    "us_domination\n",
    "\n",
    "us_domination_list = us_domination['stopword_removed_headline_text']\n",
    "us_domination_list.values.tolist()\n",
    "\n",
    "from itertools import chain\n",
    "us_domination_list = list(chain.from_iterable(us_domination_list))\n",
    "\n",
    "from collections import Counter\n",
    "us_domination_count = Counter(us_domination_list).most_common(10)\n",
    "character = []\n",
    "char_values = []\n",
    "for x in us_domination_count:\n",
    "    character.append(x[0])\n",
    "    char_values.append(x[1])\n",
    "us_domination_mapping = pd.DataFrame(character,columns =['Character'])\n",
    "us_domination_mapping[\"char_count\"] = char_values\n",
    "us_domination_mapping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations: On analysing the top 5 common words year-wise, it is evident that between the year 2015 and 2019 same set of words like police, man, says, new and (trump + us) are found repeating. Between the year 2010-2014 set of words like interview, new, police, man, says are found repeating. Among that two set of analysis, 'interview' and 'trump' are found to be different. Specifically in the year 2011, flood was found unique."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VISUALISATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Articles per Year')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Number of articles by year \n",
    "%matplotlib inline\n",
    "\n",
    "with sns.color_palette(\"GnBu_d\", 10):\n",
    "    ax= sns.countplot(y=\"Year\",data=reduced_data)\n",
    "    ax.set(xlabel='Number of Articles', ylabel='Year')\n",
    "plt.title(\"Number of Articles per Year\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation From the year 2010 till 2013, there was a rise in the number of articles. On the other side, 2014-2019 there was a steady decrease in the number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 20 words in the news headlines between the year 2010 to 2019')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mapping.plot(kind='bar',x='Character',y='char_count')\n",
    "\n",
    "plt.xlabel(\"common words\") \n",
    "\n",
    "plt.ylabel('Count') \n",
    "\n",
    "plt.title('Top 20 words in the news headlines between the year 2010 to 2019')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 10 words in the news headlines between the year 2015 to 2019')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "us_domination_mapping.plot(kind='bar',x='Character',y='char_count')\n",
    "\n",
    "plt.xlabel(\"common words\") \n",
    "\n",
    "plt.ylabel('Count') \n",
    "\n",
    "plt.title('Top 10 words in the news headlines between the year 2015 to 2019')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 10 words in the news headlines between the year 2010 to 2014')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "interview_mapping.plot(kind='bar',x='Character',y='char_count')\n",
    "\n",
    "plt.xlabel(\"common words\") \n",
    "\n",
    "plt.ylabel('Count') \n",
    "\n",
    "plt.title('Top 10 words in the news headlines between the year 2010 to 2014')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation It is clearly seen that the word 'interview' was the second top frequently used word during the year 2010 to 2014, whereas during the year 2015 to 2019, the word interview was not found in the top 10 list. Which means ABC has reduced the number of news relating to interview, conversation, debate and so on.\n",
    "\n",
    "INSIGHTS\n",
    "\n",
    "Common national conversation topics are identified as top news headlines over a certain period say for eg: Year 2011 has a unique word 'flood' which was a national conversation topic during that period.\n",
    "Crime and its related words like police, man, death, murder, charged, court are considered to be a constant topic in national conversation\n",
    "A news headline can be considered as a hot news, if it is dominating the current national conversation.\n",
    "Reduce in the number of interviews may reduced the number of articles. \n",
    "ABC may identified australians loosing their interest towards interviews, which may be the reason for ABC to reduce interviews related headlines after 2014."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------------------END OF NOTEBOOK---------------------------------------------------"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}