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FinalProject.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Analysis Final Project\n",
"This notebook consists of the analysis of the news article data.\n",
"\n",
"*Author*: Emily Kauffman"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1: Problem Definition\n",
"For this project, I will be reviewing tweets from President Donald Trump where he attacks the media for bias on particular issues and using data analytics concepts from class determine if the claims have any validity. I will determine whether or not the news organizations that are publishing the articles to which he is referring are ones that he considers “Fake News Media” (CNN, NBC, AP, NYT, ABC, CBS, Washington Post), and which are not (Fox News and affiliates)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<hr>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2: Identifying Text Sources"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# import necessary packages\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import requests # `pip install requests` if not already installed\n",
"import json\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"import nltk\n",
"from nltk.corpus import stopwords\n",
"from nltk.tokenize import word_tokenize\n",
"import ssl\n",
"import numpy as np\n",
"import webhoseio\n",
"import array\n",
"import os\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from sklearn.metrics import confusion_matrix\n",
"import seaborn as sns; sns.set()\n",
"from wordcloud import WordCloud"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Article Data\n",
"“To any of the pundits or talking heads that do not give us proper credit for this great Midterm Election, just remember two words - FAKE NEWS!”\n",
"https://twitter.com/realDonaldTrump/status/1060153052676702208\n",
"\n",
"<strong>Tweet summary</strong>: Implying that the news will not give the republican party credit for their midterm performance.\n",
"\n",
"<strong>Goal</strong>: Analyze media coverage of Republican party achievements during midterm election versus media coverage of Democrat party achievements. Analyze whether coverage of each was positive or negative. Analyze which news sources contributed the positive/negative coverage."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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],
"source": [
"# ONLY UNCOMMENT AND RUN THIS ONCE - IT WILL TAKE APPROXIMATELY 5 MINUTES\n",
"articles = None\n",
"\n",
"# read in the data and convert it into a dataframe\n",
"for subdir, dirs, files in os.walk('./party_articles'):\n",
" for file in files:\n",
" filepath = subdir + os.sep + file\n",
" if filepath.endswith(\".json\"):\n",
"# print (filepath)\n",
" with open(filepath) as f:\n",
" data = json.load(f)\n",
" df = pd.DataFrame.from_dict([data])\n",
" if articles is not None:\n",
" articles = articles.append(df, ignore_index=True, sort=True)\n",
" else:\n",
" articles = pd.DataFrame.from_dict(df)\n",
"articles.to_json('complete_articles.json')\n",
"\n",
"# to reload articles from saved json, use this instead\n",
"articles = pd.read_json('complete_articles.json', typ='frame', orient='dict')\n",
"articles.describe()\n",
"# articles.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<hr>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3: Text Organization\n",
"This step involves cleaning and preprocessing the data."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'pd' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-3756d58b730e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0marticles\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'complete_articles.json'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'frame'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morient\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'dict'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# parse published col to hold dates for better querying\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0marticles\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'published'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marticles\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'published'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
]
}
],
"source": [
"articles = pd.read_json('complete_articles.json', typ='frame', orient='dict')\n",
"\n",
"# parse published col to hold dates for better querying\n",
"articles['published'] = pd.to_datetime(articles['published'])\n",
"\n",
"articles = articles.drop(columns=[\n",
" 'url', 'highlightText',\n",
" 'external_links', 'entities', 'highlightTitle',\n",
" 'language', 'ord_in_thread', 'entities', 'crawled'\n",
"])\n",
"\n",
"for index, row in articles.iterrows():\n",
" content = json.loads(json.dumps(row['thread']))\n",
" site = content['site']\n",
" articles.loc[index, 'site'] = site\n",
" \n",
"articles = articles[articles['site'] != '']\n",
"articles = articles[articles['title'] != '']\n",
"articles = articles[articles['author'] != '']\n",
"\n",
"print(len(articles))\n",
"\n",
"# drop duplicated titles so they aren't counted twice\n",
"articles = articles.drop_duplicates(subset='title')\n",
"print(len(articles))\n",
"\n",
"articles.reset_index(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"index int64\n",
"author object\n",
"locations object\n",
"organizations object\n",
"persons object\n",
"published datetime64[ns]\n",
"text object\n",
"thread object\n",
"title object\n",
"uuid object\n",
"site object\n",
"dtype: object"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# articles.describe()\n",
"articles.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to /home/kauff/nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n",
"[nltk_data] Downloading package punkt to /home/kauff/nltk_data...\n",
"[nltk_data] Package punkt is already up-to-date!\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Remove stopwords\n",
"\n",
"ssl._create_default_https_context = ssl._create_unverified_context\n",
"nltk.download('stopwords')\n",
"nltk.download('punkt')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# set the stopwords language\n",
"stop_words = stopwords.words('english')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have to go through every word in the articles and compare their values to the list of stopwords\n",
"If they match, then it will be removed."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"clean_article_data = articles\n",
"\n",
"# loop through each content section\n",
"for index, row in articles.iterrows():\n",
" \n",
" # the content column refers to the body of the article\n",
" content = row['title']\n",
" \n",
" # tokenize the article content\n",
" words = word_tokenize(content)\n",
" \n",
" # remove stop words\n",
" clean_words = [word for word in words if word not in stop_words]\n",
"\n",
" # convert tokenized word back into the content\n",
"# clean_pre_election_data[index]['content'] = ' '.join(clean_words)\n",
" clean_article_data.loc[index, 'title'] = ' '.join(clean_words)\n",
"\n",
"# pre_election_df\n",
"# print(clean_article_data['title'])\n",
"clean_article_data.to_csv('clean_article_data.csv')\n",
"\n",
"# clean_article_data = pd.read_csv('clean_article_data.csv')\n",
"# print(clean_article_data['author'])"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 Wisconsin Republicans Are Bending Over Backwar...\n",
"1 California Democrats rewrite voting rules favo...\n",
"2 Republican North Carolina Election Fraud , Wis...\n",
"3 Laura Ingraham Mocks Taylor Swift Over Tenness...\n",
"4 Midterm elections latest : Republicans control...\n",
"6 Health care help Democrats next election\n",
"7 Mitch McConnell advances Senate vote GOP ‘ seg...\n",
"8 Republican wins Mississippi Senate vote marked...\n",
"11 Liberals blame white women Democrats ’ failure...\n",
"12 Wisconsin Republicans vote weakening governor ...\n",
"13 Midterm elections : Voting machine automatical...\n",
"14 Barbara Comstock Loses Re-election To Democrat...\n",
"15 Defeated GOP congressman blames McCain 's Obam...\n",
"16 House Democrats ' 1st bill targets big donors ...\n",
"20 How North Carolina Republicans Allegedly Hatch...\n",
"21 Election 2018 : Democrat Katie Porter overtake...\n",
"22 GOP Audits Elections Office In County That Swu...\n",
"23 Nancy Pelosi : Democrats vow vote House speake...\n",
"24 In Midterms , Republicans Paid Price For Loyal...\n",
"25 Longtime John McCain Aide Urges Americans To '...\n",
"26 Trump tells confidants Ryan blame Ryan Republi...\n",
"27 Democrats Refuse To Let Rick Scott Steal The F...\n",
"28 Democrats threaten block Republican taking sea...\n",
"29 Democrats Had Better Election Night Than It Fi...\n",
"30 Georgia Democrats , Abrams ' campaign files la...\n",
"31 Tax reform turns suburban Republicans midterms\n",
"32 Election 2018 : Democrats control Orange Count...\n",
"33 Democrats ' post-election battles signal large...\n",
"39 Republicans plan vote Tuesday curbing powers G...\n",
"68 ‘ Heads exploding ’ : Chris Wallace reacts Fox...\n",
" ... \n",
"119 2018 Election Results : Democrats Regain Contr...\n",
"120 Wisconsin GOP set vote stripping power incomin...\n",
"121 Midterm Elections 2018 : Republicans Keep Cont...\n",
"123 Election results : Democrats take House , GOP ...\n",
"124 WATCH : Republican activists harass black peop...\n",
"127 Democrat withdraws concession House race North...\n",
"128 US midterms : Democrats flip House Representat...\n",
"131 Democrats take control House : TKTK US congres...\n",
"132 Fox News panel : There ’ reason vote Republica...\n",
"133 Republicans 'Won ' Midterm Elections , Nearly ...\n",
"136 Midterm elections latest : Democrats win back ...\n",
"137 Republicans Wisconsin Michigan trying undo ele...\n",
"138 How Democrats Took The House On Election Night...\n",
"139 Texas juvenile judge frees defendants losing r...\n",
"140 Midterm elections polls botch key races turnou...\n",
"141 Billionaire Tom Steyer Happy Election After Sp...\n",
"142 Video : Poll worker caught advising 'Vote Demo...\n",
"143 Election : Democrat Katie Porter defeats GOP R...\n",
"146 For Democrats , The Midterms Are About Health ...\n",
"147 Democrat Jennifer Wexton wins Virginia congres...\n",
"149 Stacey Abrams Democrats Want Brian Kemp—or , B...\n",
"150 More 75 percent Jews voted Democrats midterm\n",
"151 Texas Democrat Celebrates Re-Election Jail Cell\n",
"152 Georgia Democrats , Abrams ' campaign files la...\n",
"153 Election 2018 : Democrat Gil Cisneros takes le...\n",
"155 It Cost Millions Fly Trump Around Country Camp...\n",
"158 N.J. Election 2018 : Tom Malinowski defeats Le...\n",
"159 Democrats find ‘ thousands new votes ’ Georgia...\n",
"161 Democrats leading key House races , Republican...\n",
"162 Mid-term elections 2018 : US Democrats win Hou...\n",
"Name: title, Length: 86, dtype: object"
]
},
"execution_count": 176,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The data will be split into two sets: one with every article published prior to\n",
"# and including election day, and one with every article published after election day\n",
"pre_election_df = clean_article_data[clean_article_data['published'] <= '2018-11-06']\n",
"post_election_df = clean_article_data[clean_article_data['published'] > '2018-11-06']\n",
"post_election_df['title']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 4: Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pre-election data index 77\n",
"author 77\n",
"locations 77\n",
"organizations 77\n",
"persons 77\n",
"published 77\n",
"text 77\n",
"thread 77\n",
"title 77\n",
"uuid 77\n",
"site 77\n",
"dtype: int64\n",
"\n",
"\n",
"post-election data index 86\n",
"author 86\n",
"locations 86\n",
"organizations 86\n",
"persons 86\n",
"published 86\n",
"text 86\n",
"thread 86\n",
"title 86\n",
"uuid 86\n",
"site 86\n",
"dtype: int64\n"
]
}
],
"source": [
"# Initial Statistical Summary of Pre-Election Articles\n",
"print('pre-election data', pre_election_df.count())\n",
"print('\\n')\n",
"print('post-election data', post_election_df.count())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The next step is to create a TFID vectorization model of the content."
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['lou', 'hearings', 'gop', 'governor', 'grab', 'graham', 'group', 'gubernatorial', 'hacking', 'half', 'harris', 'heidi', 'gil', 'heitkamp', 'helping', 'here', 'higher', 'hijacked', 'hinted', 'holier', 'hope', 'houston', 'gohmert', 'game', 'mysterious', 'fans', 'expect', 'explosive', 'eyes', 'facebook', 'facing', 'factor', 'fair', 'false', 'falsehoods', 'farthest', 'full', 'fight', 'finds', 'fired', 'first', 'flag', 'florida', 'food', 'fox', 'free', 'hunters', 'hunting', 'hypocrites', 'michigan']\n"
]
}
],
"source": [
"# create new tfid vectorizer model\n",
"pre_election_model = TfidfVectorizer()\n",
"\n",
"# fit data to model\n",
"pre_election_TFIDF = pre_election_model.fit_transform(pre_election_df['title'].values.astype('U'))\n",
"\n",
"# features\n",
"top_n = 50\n",
"pre_election_grams = pre_election_model.get_feature_names()\n",
"pre_election_indices = np.argsort(pre_election_model.idf_)[::-1]\n",
"top_pre_election_grams = [pre_election_grams[i] for i in pre_election_indices[:top_n]]\n",
"print(top_pre_election_grams)\n",
"\n",
"# convert to a dataframe\n",
"pre_election_results = pd.DataFrame(pre_election_TFIDF.toarray(), columns=pre_election_grams)\n",
"\n",
"# output for later use\n",
"pre_election_results.to_csv('tfid_pre_election_results.csv')\n",
"\n",
"# pre_election_results"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {},
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"['yet', 'emmanuel', 'hatched', 'have', 'he', 'heads', 'help', 'her', 'hope', 'houston', 'illinois', 'immigration', 'incoming', 'indiana', 'ingraham', 'instead', 'interview', 'investigates', 'investigation', 'is', 'jail', 'harvesting', 'harass', 'happy', 'flips', 'ever', 'evers', 'expire', 'exploding', 'failures', 'favor', 'fight', 'fivethirtyeight', 'fly', 'happened', 'france', 'frees', 'giving', 'gov', 'grab', 'had', 'hale', 'half', 'jay', 'jefferson', 'jews', 'mid', 'majority', 'make', 'malinowski']\n"
]
},
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" <th>8</th>\n",
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" <th>9</th>\n",
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" <th>10</th>\n",
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" <th>18</th>\n",
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" <th>19</th>\n",
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" <th>20</th>\n",
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" <th>21</th>\n",
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"</table>\n",
"<p>86 rows × 431 columns</p>\n",
"</div>"
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"\n",
"[86 rows x 431 columns]"
]
},
"execution_count": 197,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create new tfid vectorizer model\n",
"post_election_model = TfidfVectorizer()\n",
"\n",
"# fit data to model\n",
"post_election_TFIDF = post_election_model.fit_transform(post_election_df['title'].values.astype('U'))\n",
"\n",
"# features\n",
"top_n = 50\n",
"post_election_grams = post_election_model.get_feature_names()\n",
"post_election_indices = np.argsort(post_election_model.idf_)[::-1]\n",
"top_post_election_grams = [post_election_grams[i] for i in post_election_indices[:top_n]]\n",
"print(top_post_election_grams)\n",
"\n",
"# convert to a dataframe\n",
"post_election_results = pd.DataFrame(post_election_TFIDF.toarray(), columns=post_election_grams)\n",
"\n",
"# output for later use\n",
"post_election_results.to_csv('tfid_post_election_results.csv')\n",
"\n",
"post_election_results"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'top_pre_election_grams' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-6dd4b0ab8b79>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# do cool word maps of most used pre/post words\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# test = WordCloud().generate(json.dumps(top_post_election_grams))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mwordcloud\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mWordCloud\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_font_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtop_pre_election_grams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwordcloud\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bilinear'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"on\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'top_pre_election_grams' is not defined"
]
}
],
"source": [
"# do cool word maps of most used pre/post words\n",
"# test = WordCloud().generate(json.dumps(top_post_election_grams))\n",
"wordcloud = WordCloud(max_font_size=40).generate(json.dumps(top_pre_election_grams))\n",
"plt.imshow(wordcloud, interpolation='bilinear')\n",
"plt.axis(\"off\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 199,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"wordcloud = WordCloud(max_font_size=40).generate(json.dumps(top_post_election_grams))\n",
"plt.imshow(wordcloud, interpolation='bilinear')\n",
"plt.axis(\"off\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[61, 68, 55, 8, 0, 27, 0, 1, 1, 0, 0, 0]\n",
"[73, 80, 55, 10, 2, 44, 0, 5, 1, 0, 3, 3]\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# pyramid plot\n",
"keywords = [\n",
" \"republican\",\n",
" \"democrat\",\n",
" \"trump\",\n",
" \"blue wave\",\n",
" \"red wave\",\n",
" \"gop\",\n",
" \"republican win\",\n",
" \"democratic win\",\n",
" \"gop win\",\n",
" \"defeated gop\",\n",
" \"defeated dem\",\n",
" \"defeated repub\"\n",
"]\n",
"\n",
"pre_counts = []\n",
"post_counts = []\n",
"\n",
"for word in keywords:\n",
" pre_matches = pre_election_df.loc[pre_election_df['text'].str.lower().str.contains(word)]\n",
" pre_counts.append(pre_matches['index'].count())\n",
" post_matches = post_election_df.loc[post_election_df['text'].str.lower().str.contains(word)]\n",
" post_counts.append(post_matches['index'].count())\n",
"\n",
"print(pre_counts)\n",
"print(post_counts)\n",
"\n",
"y = range(len(keywords))\n",
"x1 = pre_counts\n",
"x2 = post_counts\n",
"\n",
"fig, axes = plt.subplots(ncols=2, sharey=True)\n",
"axes[0].barh(y, x1, align='center', color='gray')\n",
"axes[0].set(title='Pre-election')\n",
"\n",
"axes[1].barh(y, x2, align='center', color='gray')\n",
"axes[1].set(title='Post-election')\n",
"\n",
"axes[0].invert_xaxis()\n",
"axes[0].set(yticks=y, yticklabels=[])\n",
"\n",
"for yloc, word in zip(y, keywords):\n",
" axes[0].annotate(word, (0.5, yloc), xycoords=('figure fraction', 'data'), ha='center', va='center')\n",
"axes[0].yaxis.tick_right()\n",
"\n",
"for ax in axes.flat:\n",
" ax.margins(0.03)\n",
" ax.grid(True)\n",
"\n",
"fig.tight_layout()\n",
"fig.subplots_adjust(wspace=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<hr>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 5: Feature Analysis"
]
},
{
"cell_type": "code",
"execution_count": 240,
"metadata": {},
"outputs": [],
"source": [
"# create a training set of 100 articles for the model\n",
"train_titles = clean_article_data['title'][:25].values\n",
"\n",
"# train labels\n",
"train_encoder = LabelEncoder()\n",
"train_labels = clean_article_data['site'][:25]\n",
"train_encoded_labels = train_encoder.fit_transform(train_labels)"
]
},
{
"cell_type": "code",
"execution_count": 241,
"metadata": {},
"outputs": [],
"source": [
"# create a test set of articles for the model\n",
"test_titles = clean_article_data['title'][50:60].values\n",
"\n",
"# test labels\n",
"test_encoder = LabelEncoder()\n",
"test_labels = clean_article_data['site'][50:60]\n",
"test_encoded_labels = test_encoder.fit_transform(test_labels)"
]
},
{
"cell_type": "code",
"execution_count": 242,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Pipeline(memory=None,\n",
" steps=[('tfidfvectorizer', TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
" dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
" lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
" ngram_range=(1, 1), norm='l2', preprocessor=None, smooth_i... vocabulary=None)), ('multinomialnb', MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))])"
]
},
"execution_count": 242,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Multinomial Bayes Classifer\n",
"mnb_model = make_pipeline(TfidfVectorizer(), MultinomialNB())\n",
"\n",
"# fit to model\n",
"mnb_model.fit(train_titles, train_encoded_labels)"
]
},
{
"cell_type": "code",
"execution_count": 244,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x1080 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# predict the labels\n",
"predicted_labels = mnb_model.predict(test_titles)\n",
"predicted_labels\n",
"\n",
"mat = confusion_matrix(test_encoded_labels, predicted_labels)\n",
"\n",
"fig, ax = plt.subplots(figsize=(15,15))\n",
"sns.heatmap(mat.T, fmt='d', square=True, annot=True, cbar=False,\n",
" xticklabels=train_labels, yticklabels=test_labels, ax=ax)\n",
"plt.xlabel('true label')\n",
"plt.ylabel('predicted label');"
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {},
"outputs": [],
"source": [
"# Define a function to return the category (class) of any string:\n",
"\n",
"def predict_author(s_to_predict, train=train_titles, model=mnb_model):\n",
" \n",
" #We use our model to predict the label (class) of the string \"s\"\n",
"\n",
" pred = mnb_model.predict([s_to_predict]) \n",
" \n",
" #Return only the class of the predicted value\n",
" answer = ('Predicted author for \"' + s_to_predict + '\" is \"' + train_labels[pred[0]] + '\".' )\n",
" return answer"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Predicted author for \"north carolina board calls political operative a \\'person of interest\\' in election fraud probe\" is \"tmz.com\".'"
]
},
"execution_count": 207,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predict_author('north carolina board calls political operative a \\'person of interest\\' in election fraud probe')"
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Predicted author for \"democrat success\" is \"yahoo.com\".'"
]
},
"execution_count": 211,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predict_author('blue wave')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<hr>"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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