def test_multiple_topics_with_stop_words(self):
article = u"the banana a banana an banana or orange and orange"
topics = get_topics(article)
self.assertEqual(len(topics), 2)
self.assertTrue("banana" in topics)
self.assertTrue("orange" in topics)
self.assertFalse("the" in topics)
def test_multiple_topics_with_stop_words(self):
article = u"the banana a banana an banana or orange and orange"
topics = get_topics(article)
self.assertEqual(len(topics), 2)
self.assertTrue("banana" in topics)
self.assertTrue("orange" in topics)
self.assertFalse("the" in topics)
def get_synonyms(word):
#Clean input word (lowercase, stemming, ect)
# res = topics.get_topics(word)
cleanedWord = word
#Load trained model, !-CHANGE PATH TO PATH OF MODEL-!
model = word2vec.load(
"/Users/thiagolobo/Desktop/irproject/repo_new/stalkr/stalkr/vectors.bin"
)
try:
#get similar words from model
indexes, metrics = model.cosine(cleanedWord)
synonyms = model.vocab[indexes]
#Clean data
string = ""
for elem in synonyms:
string = string + " " + elem
cleanedSynonyms = topics.get_topics(string)
#remove duplicate of search word
wordList = list(cleanedSynonyms)
while cleanedWord in wordList:
wordList.remove(cleanedWord)
except KeyError as e:
#return empty list if word was not present in model
wordList = []
return wordList
def test_punctuation(self):
article = u"the cat was on the mat."
topics = get_topics(article)
self.assertEqual(len(topics), 2)
self.assertTrue("cat" in topics)
self.assertTrue("mat" in topics)
self.assertFalse("." in topics)
def test_punctuation(self):
article = u"the cat was on the mat."
topics = get_topics(article)
self.assertEqual(len(topics), 2)
self.assertTrue("cat" in topics)
self.assertTrue("mat" in topics)
self.assertFalse("." in topics)
def tokenize(query):
tokens = topics.get_topics(query)
tokenized = {}
for token in tokens:
if token in tokenized:
tokenized[token] += 1
else:
tokenized[token] = 1
return tokenized
def tokenize(query):
tokens = topics.get_topics(query)
tokenized = {}
for token in tokens:
if token in tokenized:
tokenized[token] += 1
else:
tokenized[token] = 1
return tokenized
def list_topics(area_id):
if users.login_id() == 0:
return render_template("index.html", login="******")
else:
area_name = areas.get_areaname(area_id)
tlist = topics.get_topics(area_id)
return render_template("topics.html",
area_id=area_id,
area_name=area_name,
topics=tlist)
def __init__(self, grouped_tweets_csv_filename, plot_year):
self.grouped_tweets_csv_filename = grouped_tweets_csv_filename
self.year = plot_year
self.topics = t.get_topics()
self.female_topics = [
'womens_rights', 'education', 'healthcare', 'familycare'
]
self.male_topics = [
'military', 'economics', 'foreign_affairs', 'policing'
]
self.party_and_gender_colors = t.get_colors('gender_and_party')
self.gender_colors = t.get_colors('gender')
self.party_colors = t.get_colors('party')
def main():
print('Loading corpus...')
chapters = load_corpus()
print('Finding named entities...')
entities_list = get_named_entities(chapters)
entities_list = filter_entities(entities_list)
print('Processing corpus...')
chapters_processed = preprocess(chapters)
print('Creating TermDocumentMatrix...')
vocabulary, vector_space = get_termDocumentMatrix(chapters_processed)
print('Computing entities relationships...')
entity_matrix = get_entity_matrix(entities_list, vocabulary, vector_space)
print('Topic extraction...')
topics = get_topics(chapters_processed)
print('Entities graph...')
draw_entities_relation(entity_matrix, topics)
def get(self):
cypher = self.db.cypher
query = self.get_argument('q')
alpha = float(self.get_argument('a', 0.5))
prtype = self.get_argument('t', 'rankb')
limit = int(self.get_argument('l', 50))
users = recommend(query, alpha=alpha, pr_type=prtype, limit=limit)
tokens = get_topics(query)
rawtokens = query.split(" ")
# Get synonyms for all tokens present in supplied query and flatten the
# result into a single list.
allsyn = sum([get_synonyms(token) for token in rawtokens], [])
# Quote all synonyms.
allsyn = [ "'" + s + "'" for s in allsyn]
# Only keep the synonyms that exists in the database.
cursor = self.db.cypher.execute("MATCH (w:Word) WHERE w.name IN [" + ",".join(allsyn) + "] RETURN w.name")
synonyms = [w["w.name"] for w in cursor]
res = {'users': users, 'tokens': tokens, 'synonyms': synonyms}
self.write(res)
def get(self):
cypher = self.db.cypher
query = self.get_argument('q')
alpha = float(self.get_argument('a', 0.5))
prtype = self.get_argument('t', 'rankb')
limit = int(self.get_argument('l', 50))
users = recommend(query, alpha=alpha, pr_type=prtype, limit=limit)
tokens = get_topics(query)
rawtokens = query.split(" ")
# Get synonyms for all tokens present in supplied query and flatten the
# result into a single list.
allsyn = sum([get_synonyms(token) for token in rawtokens], [])
# Quote all synonyms.
allsyn = ["'" + s + "'" for s in allsyn]
# Only keep the synonyms that exists in the database.
cursor = self.db.cypher.execute("MATCH (w:Word) WHERE w.name IN [" +
",".join(allsyn) + "] RETURN w.name")
synonyms = [w["w.name"] for w in cursor]
res = {'users': users, 'tokens': tokens, 'synonyms': synonyms}
self.write(res)
from datetime import datetime
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import topics as t
topics = t.get_topics()
topic_colors = t.get_colors('topics')
topic_gendered_colors = t.get_colors('topics_gendered')
def replace_party(party):
if party == 'Republican Party':
return 'Republican'
elif party == 'Democratic Party':
return 'Democratic'
else:
return party
def date_to_month_and_year(date_string):
date_string = standardize_date_string(date_string)
month, day, year = date_string.split('/')
month_and_year = month + '/' + year
return month_and_year
def standardize_date_string(date_string):
ret = ''
split_string = date_string.split('/')
"""Test topic matching and the like"""
import os
import unittest
import lib
import topics
os.chdir(lib.get_project_dir())
topic_list = topics.get_topics()
class TestMatching(unittest.TestCase):
def test_matching(self):
title_list = "the quick brown fox".split()
for word in title_list:
self.assertTrue(topics.matching(word, title_list))
for word in title_list:
self.assertTrue(topics.matching(word[1:-1], title_list))
self.assertTrue(topics.matching("", []))
self.assertTrue(topics.matching("unicode", ["unicode?"]))
if __name__ == "__main__":
unittest.main()
def main():
only_max_topics = False # true for only selecting max topic(s), false otherwise for all topics present
skip_officials = False # make true to not include official twitters in analysis
threshold_percent = 0.1
input_csv = 'C:\\Users\\Eleanor\\Dropbox\\Honors Research\\Tweets\\TwitterAPIWrapper\\tweets_2018.csv'
# input_csv = 'sample_tweets.csv'
output_csv = '2018_categorized_tweets_all_2.csv'
topics = t.get_topics()
start = time.time()
print('reading')
tweets_df = pd.read_csv(input_csv)
print('cleaning')
clean_data(tweets_df)
if skip_officials:
tweets_df = tweets_df[tweets_df['twitter_type'] != 'official_twitter']
tweets_df['category'] = ''
tweets_df['category_word_matches'] = ''
tweets_df['category_topic_percent'] = 0
for topic in topics:
tweets_df[topic] = 0
print('processing')
for index, row in tweets_df.iterrows():
if index % 1000 == 0:
print(index)
counter = {topic: 0 for topic in topics.keys()}
counter_words = {topic: [] for topic in topics.keys()}
for topic in topics:
for phrase in topics[topic]:
if ' ' in phrase:
joined_clean_text = ' '.join(row['cleaned_text'])
if phrase in joined_clean_text:
if ' ' in phrase and not really_in(
joined_clean_text, phrase):
continue
counter[topic] += 1
counter_words[topic].append(phrase)
else:
if phrase in row['cleaned_text']:
counter[topic] += 1
counter_words[topic].append(phrase)
if only_max_topics:
max_topic = max(counter, key=lambda val: counter[val])
max_topic_phrases = counter_words[max_topic]
max_topic_percent = counter[max_topic] / len(
row['cleaned_text']) if len(row['cleaned_text']) != 0 else 0
else:
max_topic = ''
max_topic_phrases = []
max_topic_percent = 1.0001 * threshold_percent
if max_topic_percent <= threshold_percent:
continue
for topic in topics.keys():
if topic == max_topic:
continue
topic_percent = counter[topic] / len(row['cleaned_text']) if len(
row['cleaned_text']) != 0 else 0
if topic_percent >= max_topic_percent:
max_topic += ',' + topic
max_topic_phrases += counter_words[topic]
if max_topic == '':
continue
elif max_topic[0] == ',':
max_topic = max_topic[1:]
tweets_df.loc[index, ['category']] = max_topic
tweets_df.loc[index,
['category_word_matches']] = str(max_topic_phrases)
tweets_df.loc[index, ['category_topic_percent']] = max_topic_percent
for topic in max_topic.split(','):
tweets_df.loc[index, [topic.strip()]] = 1
print('writing')
tweets_df.to_csv(output_csv)
print(time.time() - start)
def group(categorized_tweets_csv_filename, candidates_csv_filename,
output_filename):
topics = get_topics()
categorized_tweets_df = pd.read_csv(categorized_tweets_csv_filename)
cols = [
'candidate_id', 'tweet_favorites', 'tweet_retweets', 'category'
]
new_tweets_df = pd.DataFrame(columns=cols)
for index, row in categorized_tweets_df.iterrows():
if index % 500 == 0:
print(index)
for topic in topics:
if row[topic] == 1:
new_row = {
'candidate_id': row['candidate_id'],
'tweet_favorites': row['tweet_favorites'],
'tweet_retweets': row['tweet_retweets'],
'category': topic
}
new_tweets_df = new_tweets_df.append(new_row,
ignore_index=True)
new_tweets_df['tweet_favorites'] = new_tweets_df[
'tweet_favorites'].astype(int)
new_tweets_df['tweet_retweets'] = new_tweets_df[
'tweet_retweets'].astype(int)
new_tweets_df = new_tweets_df.groupby(['candidate_id', 'category'
]).agg(['count', 'mean', 'sum'])
new_tweets_df.columns = new_tweets_df.columns.map(
'{0[0]}_{0[1]}'.format)
new_tweets_df = new_tweets_df.reset_index()
new_tweets_df.drop(['tweet_retweets_count'], axis=1, inplace=True)
new_tweets_df.columns = [
'candidate_id', 'category', 'count', 'tweet_favorites_mean',
'tweet_favorites_sum', 'tweet_retweets_mean', 'tweet_retweets_sum'
]
cols = ['candidate_id']
for topic in topics:
cols.append(topic + '_count')
cols.append(topic + '_average_favorites')
cols.append(topic + '_sum_favorites')
cols.append(topic + '_average_retweets')
cols.append(topic + '_sum_retweets')
candidate_category_info_df = pd.DataFrame(columns=cols)
current_cand_id = -1
current_row = {}
for index, row in new_tweets_df.iterrows():
if index % 500 == 0:
print(index)
if row['candidate_id'] != current_cand_id:
for topic in topics:
if topic + '_count' not in current_row:
current_row[topic + '_count'] = 0
current_row[topic + '_average_favorites'] = 0
current_row[topic + '_sum_favorites'] = 0
current_row[topic + '_average_retweets'] = 0
current_row[topic + '_sum_retweets'] = 0
if current_cand_id != -1:
candidate_category_info_df = candidate_category_info_df.append(
current_row, ignore_index=True)
current_row = {'candidate_id': row['candidate_id']}
current_cand_id = row['candidate_id']
category = row['category']
current_row[category + '_count'] = row['count']
current_row[category +
'_average_favorites'] = row['tweet_favorites_mean']
current_row[category +
'_sum_favorites'] = row['tweet_favorites_sum']
current_row[category +
'_average_retweets'] = row['tweet_retweets_mean']
current_row[category + '_sum_retweets'] = row['tweet_retweets_sum']
engagement_df = CandidateGrouper.get_engagement_df(
categorized_tweets_df)
candidates_df = pd.read_csv(candidates_csv_filename)
candidates_df = candidates_df[['candidate_id', 'party', 'gender']]
candidate_category_info_df = candidate_category_info_df.merge(
engagement_df, on='candidate_id')
candidate_category_info_df = candidate_category_info_df.merge(
candidates_df, on='candidate_id')
# This part calculates masculine/feminine engagement
feminine_topics = [
'womens_rights', 'education', 'healthcare', 'familycare'
]
masculine_topics = [
'military', 'economics', 'foreign_affairs', 'policing'
]
candidate_category_info_df['masculine_count'] = sum(
candidate_category_info_df['{0}_count'.format(topic)]
for topic in masculine_topics)
candidate_category_info_df['feminine_count'] = sum(
candidate_category_info_df['{0}_count'.format(topic)]
for topic in feminine_topics)
candidate_category_info_df['masculine_total_engagement'] = sum(
candidate_category_info_df['{0}_sum_favorites'.format(topic)]
for topic in masculine_topics) + sum(
candidate_category_info_df['{0}_sum_retweets'.format(topic)]
for topic in masculine_topics)
candidate_category_info_df['feminine_total_engagement'] = sum(
candidate_category_info_df['{0}_sum_favorites'.format(topic)]
for topic in feminine_topics) + sum(
candidate_category_info_df['{0}_sum_retweets'.format(topic)]
for topic in feminine_topics)
candidate_category_info_df[
'masculine_average_engagement'] = candidate_category_info_df[
'masculine_total_engagement'] / candidate_category_info_df[
'masculine_count']
candidate_category_info_df[
'feminine_average_engagement'] = candidate_category_info_df[
'feminine_total_engagement'] / candidate_category_info_df[
'feminine_count']
candidate_category_info_df.to_csv(output_filename)
def __init__(self, grouped_tweets_csv_filename):
self.grouped_tweets_csv_filename = grouped_tweets_csv_filename
self.topics = t.get_topics()
self.topic_colors = t.get_colors('topics')
self.last_ys = None
def process_text(data):
tokens = topics.get_topics(data)
return tokens
def test_topics_without_stop_words(self):
article = u"banana banana banana"
topics = get_topics(article)
self.assertEqual(len(topics), 1)
self.assertTrue("banana" in topics)
def test_multiple_topics_without_stop_words(self):
article = u"banana banana banana orange orange"
topics = get_topics(article)
self.assertEqual(len(topics), 2)
self.assertTrue("banana" in topics)
self.assertTrue("orange" in topics)
def test_multiple_topics_without_stop_words(self):
article = u"banana banana banana orange orange"
topics = get_topics(article)
self.assertEqual(len(topics), 2)
self.assertTrue("banana" in topics)
self.assertTrue("orange" in topics)
def test_topics_without_stop_words(self):
article = u"banana banana banana"
topics = get_topics(article)
self.assertEqual(len(topics), 1)
self.assertTrue("banana" in topics)
