def main(drugs, phase):
vectorizer = TfidfVectorizer(stop_words=ENGLISH_STOP_WORDS.union(
frozenset([
"plus", "+", "-", "|", ",", "Drug", "Radiation", "Genetic",
"Combination", "Product", "Biological", "Procedure", "Other",
"drug", "radiation", "genetic", "combination", "product",
"biological", "procedure", "other"
])))
df = pd.read_csv("SearchResults.csv").dropna()
df = df.filter(items=['NCT Number', 'Phases', 'Interventions'])
df['Phases'] = df['Phases'].apply(
lambda x: x.replace(' ', '_').replace('|', ' '))
df['Interventions'] = df['Interventions'].apply(
lambda x: x.replace('|', ' '))
df['Phase_Interventions'] = df[['Phases', 'Interventions'
]].apply(lambda x: ' '.join(x), axis=1)
X = vectorizer.fit_transform(df['Phase_Interventions'])
n_clusters = 100
kmeans = KMeans(n_clusters=n_clusters)
df['Cluster'] = kmeans.fit_predict(X)
order_centroids = kmeans.cluster_centers_.argsort()[:, ::-1]
terms = vectorizer.get_feature_names()
#for i in range(n_clusters):
#print ("Cluster %d:" % i)
#for ind in order_centroids[i, :10]:
#print (' %s' % terms[ind])
#print
pred = predict(df, kmeans, phase, drugs, vectorizer)
def stopwords(self):
fin_stopwords = []
with open(fin_stopwords_file,"rb") as csvfile:
reader = csv.reader(csvfile)
for row in reader:
fin_stopwords.extend(row)
stopwords = ENGLISH_STOP_WORDS.union(fin_stopwords)
self.stopwords = stopwords
def to_features(tweet):
stop_words = ['iphone', 'ipod', 'ipad', 'mac', 'imac', 'rt', 'apple', 'amp']
stop_words = ENGLISH_STOP_WORDS.union(stop_words)
vectorizer = CountVectorizer(min_df=1, ngram_range=(1, 2), stop_words=stop_words)
tweet = rm_usernames(rm_links(tweet))
try:
vectorizer.fit_transform([tweet])
return vectorizer.get_feature_names()
except ValueError:
return ['']
def preprocess_text(X1, y1, X2, y2, Xa, ya):
my_stop_words = read_stop_words2()
stop_words = ENGLISH_STOP_WORDS.union(my_stop_words)
pipeline = Pipeline([('vect',
CountVectorizer(stop_words=stop_words,
max_df=0.5,
ngram_range=(1, 1))),
('tfidf', TfidfTransformer())])
X1 = X1.A1
X2 = X2.A1
Xa = Xa.A1
y1 = np.array(y1).tolist()
y2 = np.array(y2).tolist()
ya = np.array(ya).tolist()
X1 = pipeline.fit_transform(X1, y1)
X2 = pipeline.transform(X2)
Xa = pipeline.transform(Xa)
return X1, y1, X2, y2, Xa, ya
def preprocess(self):
my_stop_words = read_stop_words2()
stop_words = ENGLISH_STOP_WORDS.union(my_stop_words)
self.pipeline = Pipeline([('vect',
CountVectorizer(stop_words=stop_words,
max_df=0.5,
ngram_range=(1, 1))),
('tfidf', TfidfTransformer())])
self.X1 = self.X1.A1
self.X2 = self.X2.A1
self.X3 = self.X3.A1
self.y1 = np.array(self.y1).tolist()
self.y2 = np.array(self.y2).tolist()
self.y3 = np.array(self.y3).tolist()
self.X1 = self.pipeline.fit_transform(self.X1, self.y1)
self.X2 = self.pipeline.transform(self.X2)
if self.X3.shape[0] > 0:
self.X3 = self.pipeline.transform(self.X3)
user-specifiable), and the type of taggable data will also be extended (e.g. chunk-IOB tagging, &c). """ import cPickle as pickle from datetime import datetime import logging from time import time from sklearn.linear_model import LogisticRegression from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction import DictVectorizer from sklearn.feature_extraction.stop_words import ENGLISH_STOP_WORDS from .utils.data_utils import DataLoader __authour__ = "fmailhot ([email protected])" #TODO(fmailhot): curate this list a bit more carefully stopwords = ENGLISH_STOP_WORDS.union({"did", "does", "doing", "having", "just", "theirs"}) class LogisticRegressionPOSTagger(object): def __init__(self, model_file=None, data_files=None): try: self.model = pickle.load(open(model_file, "rb")) except IOError, io_e: logging.critical(str(io_e)) raise(io_e)
"y'all'd've": "you all would have",
"y'all're": "you all are",
"y'all've": "you all have",
"you'd": "you had",
"you'd've": "you would have",
"you'll": "you you will",
"you'll've": "you you will have",
"you're": "you are",
"you've": "you have"
}
c_re = re.compile('(%s)' % '|'.join(c_dict.keys()))
add_stop = ['', ' ', 'say', 's', 'u', 'ap', 'afp', '...', 'n', '\\']
stop_words = ENGLISH_STOP_WORDS.union(add_stop)
tokenizer = TweetTokenizer()
pattern = r"(?u)\b\w\w+\b"
punc = list(set(string.punctuation))
def casual_tokenizer(text): #Splits words on white spaces (leaves contractions intact) and splits out trailing punctuation
tokens = tokenizer.tokenize(text)
return tokens
def expandContractions(text, c_re=c_re):
def replace(match):
return c_dict[match.group(0)]
return c_re.sub(replace, text)
import pandas as pd
import rethinkdb as r
import re
articles = get_articles(lambda join: {
'body': join['left']['body'],
'title': join['left']['title'],
'credible': join['right']['isReliable']
})
df = pd.DataFrame.from_records(articles)
with open('./tl_stopwords.txt', 'r') as f:
TL_STOPWORDS = f.read().splitlines()
STOP_WORDS = ENGLISH_STOP_WORDS.union(TL_STOPWORDS)
body_tfidf = TfidfVectorizer(
token_pattern=r'(?ui)\b\w*[a-z]{2}\w*\b',
stop_words=STOP_WORDS,
ngram_range=(1, 2),
max_df=0.85,
min_df=0.01)
title_tfidf = TfidfVectorizer(
token_pattern=r'(?ui)\b\w*[a-z]{2}\w*\b',
stop_words=STOP_WORDS,
ngram_range=(1, 2),
max_df=0.85,
min_df=0.01)
def generate_models(posts, start, end):
df_posts = pd.DataFrame(posts)
# print(df_posts[['title', 'post_date']].sort_values('post_date'))
# word count
df_posts['count'] = df_posts['selftext'].str.count(' ') + 1
# remove duplicates ie spam, daily/weekly notices etc.
df_posts = df_posts.drop_duplicates(subset='selftext')
min_word_cnt = 10
max_word_cnt = 1500
# remove extra-short and extra-long posts
df_posts = df_posts[(df_posts['count'] >= min_word_cnt)
& (df_posts['count'] < max_word_cnt)]
# remove spam posts contains at least 5 consecutive words
df_posts = df_posts[~df_posts['selftext'].str.contains(
r'\b(\w+)(\s+\1){4,}\b', r'\1', flags=re.IGNORECASE)]
custom_stop_word_list = [
'area', 'thanks', 'place', 'state', 'people', 'time', 'year',
'day, city', 'town', 'week', 'question', 'county', 'said', 'thank',
'reddit', 'ave', 'really', 'hey', 'way', 'lot', 'thing', 'don', 'hour',
'idea', 'option', 'wa', 'does', 'ha', 'use', 'like', 'number', 'didn',
'doesn', 'google', 'sub', 'blah', 'mod', 'lol', 'hello', 'month',
'issue', 'location', 'minute', 'today', 'example', 'sunday', 'monday',
'tuesday', 'wednesday', 'thursday', 'friday', 'saturday',
'information', 'info', 'subreddit', 'wiki', 'january', 'february',
'march', 'april', 'may', 'june', 'july', 'august', 'september',
'october', 'november', 'december', 'great', 'new', 'bos', 'thx',
'shit', 'penis', 'couldn', 'f**k', 'just', 'today', 'tomorrow', 'sort',
'item', 'anybody', 'list', 'post', 'page', 'dont', 'img', 'wouldn',
'would', 'redditors', 'somebody', 'img'
]
custom_stop_words = ' '.join(custom_stop_word_list)
blob = TextBlob(custom_stop_words)
lemmatized_custom_stop_word_list = blob.words.lemmatize(
) # lemmatize all custom stop words
stop_words = ENGLISH_STOP_WORDS.union(lemmatized_custom_stop_word_list)
def preprocess_text(post):
"""This is run before the sentiment analysis"""
# remove | words |
regex_pat = re.compile(r'\|.+?\|', flags=re.IGNORECASE)
post = re.sub(regex_pat, '', post)
# remove { words }
regex_pat = re.compile(r'{.+?}', flags=re.IGNORECASE)
post = re.sub(regex_pat, '', post)
# remove ( words )
regex_pat = re.compile(r'\(.+?\)', flags=re.IGNORECASE)
post = re.sub(regex_pat, '', post)
# remove [ words ]
regex_pat = re.compile(r'\[.+?\]', flags=re.IGNORECASE)
post = re.sub(regex_pat, '', post)
# remove links
regex_pat = re.compile(r'https?:\/\/.*[\r\n]*', flags=re.IGNORECASE)
post = re.sub(regex_pat, '', post)
# remove emails
regex_pat = re.compile(r'\S*@\S*\s?', flags=re.IGNORECASE)
post = re.sub(regex_pat, '', post)
# remove any digits or words that start with digits ie 19th
regex_pat = re.compile(r'\b\D?\d.*?\b', flags=re.IGNORECASE)
post = re.sub(regex_pat, ' ', post)
# remove words that start # ie #x200b
regex_pat = re.compile(r'#.*\b', flags=re.IGNORECASE)
post = re.sub(regex_pat, ' ', post)
post = post.replace('&', 'and').replace(' ', ' ')
post = post.replace('’',
"'").replace('‘',
"'").replace('“',
'"').replace('”', '”')
# remove special stop words
regex_pat = re.compile(
r'update:|wibta|email|gmail|tldr|nbsp|\b.+_.+\b|\baaa.+\b|\bbbb.+\b',
flags=re.IGNORECASE)
post = re.sub(regex_pat, '', post)
# collapse spaces
regex_pat = re.compile(r'\s+', flags=re.IGNORECASE)
post = re.sub(regex_pat, ' ', post)
return post
def get_topics(nmf_model, num_keywords):
num_topics = nmf_model.components_.shape[0]
topics_dict = {}
for ix, topic in enumerate(nmf_model.components_):
topics_dict[ix] = ", ".join([
count_vectorizer.get_feature_names()[i]
for i in topic.argsort()[:-num_keywords - 1:-1]
])
return topics_dict
# Preprocess the posts:
df_posts['selftext_preprocessed'] = df_posts['selftext'].apply(
preprocess_text)
sid = SentimentIntensityAnalyzer()
df_posts['sentiment_all'] = df_posts['selftext_preprocessed'].apply(
lambda x: sid.polarity_scores(x))
# extract compound score to new column
df_posts['sentiment_compound'] = df_posts['sentiment_all'].apply(
lambda x: x.get('compound'))
# Extra step needed to calculate state sentiment since all cities within that state should be included as well
df_cities_tmp = df_cities[['city_sub', 'state_fip']]
df_cities_tmp = df_cities_tmp.rename(columns={'city_sub': 'subreddit'})
df_states_tmp = df_states[['state_sub', 'state_fip']]
df_states_tmp = df_states_tmp.rename(columns={'state_sub': 'subreddit'})
df_posts_tmp = df_posts[['subreddit', 'geo_type', 'sentiment_compound']]
df_posts_cities = pd.merge(df_posts_tmp, df_cities_tmp, on='subreddit')
df_posts_states = pd.merge(df_posts_tmp, df_states_tmp, on='subreddit')
frames = [df_posts_cities, df_posts_states]
df_posts_sent_tmp = pd.concat(frames)
# Create sentiment df for each subreddit
df_sent = df_posts.groupby('subreddit') \
.agg({'sentiment_compound':'mean', 'geo_type':'first'}) \
.reset_index()
sent_dict = df_sent.to_dict('records')
# Create sentiment df for each subreddit
df_sent_cities = df_posts_cities.groupby('subreddit') \
.agg({'sentiment_compound':'mean'}) \
.reset_index()
sent_dict_cities = df_sent_cities.to_dict('records')
# special case for states to inlucde states and cities with state
df_sent_states = df_posts_sent_tmp.groupby('state_fip') \
.agg({'sentiment_compound':'mean'}) \
.reset_index()
sent_dict_states = df_sent_states.to_dict('records')
quant_states_25 = round(df_sent_states.quantile(.25).values[1], 2)
quant_states_75 = round(df_sent_states.quantile(.75).values[1], 2)
quant_cities_25 = round(df_sent_cities.quantile(.25).values[0], 2)
quant_cities_75 = round(df_sent_cities.quantile(.75).values[0], 2)
connection_args = {
'host': os.getenv('POSTGRES_HOST'),
'user': os.getenv('POSTGRES_USER'),
'dbname': os.getenv('POSTGRES_RP_DB'),
'port': os.getenv('POSTGRES_PORT')
}
connection = pg.connect(**connection_args)
# insert sentiments scores into Postgres
cursor = connection.cursor()
for row in sent_dict_states:
state_fip = row['state_fip']
sentiment_compound = round(row['sentiment_compound'], 2)
if sentiment_compound > quant_states_75:
sentiment_rating = 'pos'
elif sentiment_compound < quant_states_25:
sentiment_rating = 'neg'
else:
sentiment_rating = 'neu' # neutral
query = "UPDATE states SET sentiment_compound = %s, sentiment_rating = %s WHERE state_fip = %s"
data = (sentiment_compound, sentiment_rating, state_fip)
cursor.execute(query, data)
connection.commit()
cursor.close()
# insert sentiments scores into Postgres
cursor = connection.cursor()
for row in sent_dict_cities:
subreddit = row['subreddit']
sentiment_compound = round(row['sentiment_compound'], 2)
if sentiment_compound > quant_cities_75:
sentiment_rating = 'pos'
elif sentiment_compound < quant_cities_25:
sentiment_rating = 'neg'
else:
sentiment_rating = 'neu' # neutral
query = "UPDATE cities SET sentiment_compound = %s, sentiment_rating = %s WHERE city_sub = %s"
data = (sentiment_compound, sentiment_rating, subreddit)
cursor.execute(query, data)
connection.commit()
cursor.close()
def compute_ascore(geo):
cursor = connection.cursor()
if geo == 'states':
table = 'states'
sub_field = 'state_sub'
else:
table = 'cities'
sub_field = 'city_sub'
query = f'SELECT {sub_field}, pop_2018, median_hh_income, sentiment_compound FROM {table}'
df_metrics = pd_sql.read_sql(query, connection)
df_metrics['ascore'] = create_ascore(
df_metrics[['pop_2018']], df_metrics[['median_hh_income']],
df_metrics[['sentiment_compound']])
df_metrics['ascore'] = df_metrics['ascore'].round(1)
for row in df_metrics.itertuples():
query = f'UPDATE {table} SET ascore = %s WHERE {sub_field} = %s'
data = (row.ascore, row[1])
cursor.execute(query, data)
connection.commit()
cursor.close()
compute_ascore('states')
compute_ascore('cities')
query = f'SELECT state_sub, sentiment_compound, sentiment_rating, ascore FROM states'
df_states_metrics = pd_sql.read_sql(query, connection)
query = f'SELECT city_sub, sentiment_compound, sentiment_rating, ascore FROM cities'
df_cities_metrics = pd_sql.read_sql(query, connection)
pickle.dump(df_states_metrics, open('data/df_states_metrics.pkl', 'wb'))
pickle.dump(df_cities_metrics, open('data/df_cities_metrics.pkl', 'wb'))
def clean_text(post):
"""Run after sentiment analysis"""
blob = TextBlob(post)
# delete 1 and 2 letter words
regex_pat = re.compile(r'\b\w{1,2}\b', flags=re.IGNORECASE)
post = re.sub(regex_pat, ' ', post)
# remove quotes
regex_pat = re.compile(r'[\'"]', flags=re.IGNORECASE)
post = re.sub(regex_pat, '', post)
# set textblob again with cleaned words:
blob = TextBlob(post, pos_tagger=nltk_tagger)
# lemmatize, lowercase, and only include nouns and proper nouns
words = [
token.lemmatize().lower()
for token, (_, pos) in zip(blob.words, blob.tags)
if pos in ['NN', 'NNS', 'NNP', 'NNPS']
]
return ' '.join(words)
df_posts['selftext_final'] = df_posts['selftext_preprocessed'].apply(
clean_text)
cursor = connection.cursor()
query_topics = "TRUNCATE topics RESTART IDENTITY CASCADE" # CASCADE needed due to foreign key
query_kw = "TRUNCATE keywords RESTART IDENTITY CASCADE"
query_geo = "TRUNCATE topics_geo RESTART IDENTITY CASCADE"
query_tkw = "TRUNCATE topics_keywords RESTART IDENTITY CASCADE"
cursor.execute(query_topics)
cursor.execute(query_kw)
cursor.execute(query_geo)
cursor.execute(query_tkw)
connection.commit()
cursor.close()
cursor = connection.cursor()
query = f"INSERT INTO models (extract_date) VALUES (to_timestamp('{start}', 'YYYY-MM-DD HH24:MI:SS')) RETURNING model_id"
cursor.execute(query)
model_id = cursor.fetchone()[0]
connection.commit()
cursor.close()
cursor = connection.cursor()
for location in sent_dict:
sub = location['subreddit']
posts_final = df_posts['selftext_final'][df_posts['subreddit'] == sub]
if len(df_states[df_states['state_sub'] == sub]):
state_name = df_states.loc[df_states['state_sub'] == sub,
'state_name'].values[0].lower()
# it's necessary to lemmatize the word otherwise:
# UserWarning: Your stop_words may be inconsistent with your preprocessing.
blob = TextBlob(state_name)
state_tokens = blob.words.lemmatize()
# Tailor stop word for each location
stop_words_tot = stop_words.union(state_tokens)
geo_type = 'state'
query = "SELECT state_id FROM states WHERE state_sub = %s"
data = (sub, ) # tuple required for cursor.execute
cursor.execute(query, data)
state_id = cursor.fetchone()[0]
geo_id = state_id
else:
city_short = df_cities.loc[df_cities['city_sub'] == sub,
'city_short'].values[0].lower()
# it's necessary to lemmatize the word otherwise:
# UserWarning: Your stop_words may be inconsistent with your preprocessing.
blob = TextBlob(city_short)
city_tokens = blob.words.lemmatize()
stop_words_tot = stop_words.union(city_tokens)
geo_type = 'city'
query = "SELECT city_id FROM cities WHERE city_sub = %s"
data = (
sub,
) # comma will turn it into a list which is required in cursor.execute
cursor.execute(query, data)
city_id = cursor.fetchone()[0]
geo_id = city_id
connection.commit()
count_vectorizer = CountVectorizer(ngram_range=(1, 2),
stop_words=stop_words_tot)
doc_word = count_vectorizer.fit_transform(posts_final)
nmf_model = NMF(num_topics)
nmf_model.fit_transform(doc_word)
topics_dict = get_topics(nmf_model, num_keywords)
for k, v in topics_dict.items():
keywords = v.split(', ') # convert keywords to list
query = "INSERT INTO topics (topic) VALUES ('Topic') RETURNING topic_id"
cursor.execute(query)
topic_id = cursor.fetchone()[0]
connection.commit()
for word in keywords:
if len(word
) <= 30: # do not include any word over 30 characters
# check whether keyword exists and if so, get the id to use
query = "SELECT keyword_id FROM keywords WHERE keyword = %s"
data = (word, )
cursor.execute(query, data)
row_kw = cursor.fetchone()
if row_kw is not None:
keyword_id = row_kw[0] # keyword exists
else:
query_kw = "INSERT INTO keywords (keyword) VALUES (%s) RETURNING keyword_id"
data_kw = (word, )
cursor.execute(query_kw, data_kw)
keyword_id = cursor.fetchone()[0]
query_kw = "INSERT INTO topics_keywords (topic_id, keyword_id) VALUES (%s, %s)"
data_kw = (topic_id, keyword_id)
cursor.execute(query_kw, data_kw)
# geo_type: city or state
# geo_id: city_id or state_id
query = "INSERT INTO topics_geo (geo_type, geo_id, topic_id, model_id) VALUES (%s, %s, %s, %s)"
data = (geo_type, geo_id, topic_id, model_id)
cursor.execute(query, data)
connection.commit()
cursor.close()
cursor = connection.cursor()
query = """
UPDATE keywords AS kw
SET num_geo = sq.count FROM (
SELECT count(tk.keyword_id) AS count, tk.keyword_id
FROM topics_keywords as tk
INNER JOIN keywords as k on k.keyword_id = tk.keyword_id
GROUP BY tk.keyword_id
) AS sq
WHERE kw.keyword_id = sq.keyword_id
"""
cursor.execute(query)
connection.commit()
cursor.close()
# EDA exploration used to create topic dictionary
# broad topics at top, narrow topics below which will override the broad ones
topics_dict = [{
'Work': ['work', 'job', 'management', 'employer', 'office']
}, {
'Entertainment': [
'museum', 'play', 'music', 'fun', 'game', 'theater', 'theatre',
'comedy', 'movie'
]
}, {
'Housing': [
'housing', 'apartment', 'lease', 'mortgage', 'rent', 'tenant',
'rent', 'household', 'condo', 'hoa', 'lease', 'landlord', 'mold',
'house'
]
}, {
'Vehicles': ['vehicle', 'suv', 'car', 'truck']
}, {
'Transportation': [
'transportation', 'car', 'road', 'train', 'highway', 'parking',
'truck', 'plane', 'traffic', 'passenger', 'driver', 'driving'
]
}, {
'Religion': ['religion', 'church', 'christ', 'jesus', 'lord']
}, {
'Alcohol': ['alcohol', 'beer', 'wine', 'brewing']
}, {
'Public Transit': [
'public', 'train', 'route', 'bus', 'passenger', 'subway', 'line',
'transfer', 'transit', 'muni', 'bart'
]
}, {
'Travel': ['travel', 'adventure', 'pack', 'camera']
}, {
'Government': ['government', 'council', 'ordinance', 'complaint']
}, {
'School':
['school', 'campus', 'class', 'student', 'transfer', 'college']
}, {
'Food': ['food', 'grocery', 'store', 'sandwich']
}, {
'Restaurants': [
'food', 'restaurant', 'seafood', 'pizza', 'eat', 'taco', 'service',
'dim sum', 'burger'
]
}, {
'College': ['college', 'university', 'engineering']
}, {
'Medicine': ['vaccine', 'doctor', 'operation', 'hospital']
}, {
'Crime': [
'crime', 'property crime', 'theft', 'robbery', 'police', 'abuse',
'homicide', 'suspect', 'offender'
]
}, {
'Drugs': ['drug', 'drug problem', 'violation', 'order', 'substance']
}, {
'Homelessness':
['homeless', 'problem', 'shelter', 'policy', 'depression']
}, {
'Jobs': ['career', 'job', 'resume', 'company', 'service', 'interview']
}, {
'Beaches': ['beach', 'sea', 'pier', 'island']
}, {
'Pizza': ['pizza', 'pepperoni', 'topping', 'pizza hut']
}, {
'Gaming': ['game', 'magic', 'video game', 'twitch']
}, {
'Pets': [
'pet', 'cat', 'dog', 'fish', 'shelter', 'animal', 'breed', 'park',
'owner', 'rabbit', 'kitten'
]
}, {
'Service Animals': ['animal', 'service animal', 'dog', 'blind']
}, {
'Business': ['industry', 'business', 'meetup', 'job']
}, {
'Night Life': ['night', 'bar', 'drink', 'downtown']
}, {
'Ridesharing': ['taxi', 'uber', 'lyft', 'jump', 'ride']
}, {
'Co-Living': ['apartment', 'share', 'room', 'roommate']
}, {
'Elections':
['election', 'volunteer', 'campaign', 'gerrymandering', 'candidate']
}, {
'Politics':
['politics', 'republican', 'democrat', 'trump', 'dems', 'gop']
}, {
'Cycling': [
'cycling', 'bike', 'lane', 'bicycle', 'ride', 'biker', 'bikers',
'bike lane', 'cyclist', 'rider'
]
}, {
'Sports': [
'sport', 'basketball', 'baseball', 'football', 'soccer',
'ballpark', 'tennis', 'season', 'team', 'game'
]
}, {
'Bars':
['alcohol', 'license', 'owner', 'pub', 'brew', 'beer', 'draft']
}, {
'Cafes': ['coffee', 'tea', 'bean']
}, {
'Health Care': ['health', 'health care', 'coverage']
}, {
'Salons': ['hair', 'haircut', 'color', 'nails', 'facial', 'barber']
}, {
'Reading': ['reading', 'book', 'library']
}, {
'Music': ['music', 'musician', 'venue']
}, {
'Desserts': ['dessert', 'ice cream', 'cake', 'pie', 'ice', 'cream']
}, {
'LGBTQ': ['LGBT', 'queer', 'gay', 'drag', 'pride', 'transgender']
}, {
'Air Transportation': ['air', 'jet', 'aircraft', 'plane']
}, {
'Parking': ['parking', 'garage']
}, {
'Tatoos': ['tatoos', 'artist']
}, {
'Housing Problems': ['water', 'damage', 'water damage', 'mold']
}, {
'Smoking': ['cigarette', 'cigarettes', 'vape', 'cbd', 'cannabis']
}, {
'Marijuana':
['cbd', 'marijuana', 'dispensary', 'weed', 'cannabis', 'cbd oil']
}, {
'Disney': ['disney', 'disneyland', 'disneyworld']
}, {
'Internet Service':
['router', 'speed', 'xfinity', 'verizon', 'modem', 'router modem']
}]
threshold = .2
min_kw_topic = threshold * num_keywords
def assign_topics():
"""Loop through topics keywords and assign topics from topics_dict if minimum keyword
match threshold is met"""
cursor = connection.cursor()
query = """SELECT tk.topic_id, string_agg(k.keyword, '-:-') AS keyword_list_db
FROM keywords AS k
INNER JOIN topics_keywords AS tk ON tk.keyword_id = k.keyword_id
INNER JOIN topics_geo AS tg ON tg.topic_id = tk.topic_id
LEFT JOIN cities AS c ON c.city_id = tg.geo_id AND tg.geo_type = 'city'
LEFT JOIN states AS s ON s.state_id = tg.geo_id AND tg.geo_type = 'state'
GROUP BY tg.geo_id, tk.topic_id
"""
cursor.execute(query)
result = cursor.fetchall()
connection.commit()
for topic_id, keywords_db in result:
keywords_list_db = keywords_db.split('-:-')
# loop through defined topics
for row in topics_dict:
for topic, keywords_list in row.items():
keyword_matches = len(
set(keywords_list_db) & set(keywords_list))
if keyword_matches >= min_kw_topic:
query = "UPDATE topics SET topic = %s WHERE topic_id = %s"
data = (topic, topic_id)
cursor.execute(query, data)
connection.commit()
assign_topics()
cursor = connection.cursor()
query = """INSERT INTO topics_archive (topic, geo_type, geo_id, model_id)
(SELECT t.topic, tg.geo_type, tg.geo_id, tg.model_id
FROM topics as t
INNER JOIN topics_geo AS tg ON tg.topic_id = t.topic_id
WHERE t.topic != 'Topic'
)
"""
cursor.execute(query)
connection.commit()
query = """INSERT INTO states_archive (sentiment_compound, sentiment_rating, ascore, state_id, model_id)
(SELECT DISTINCT s.sentiment_compound, s.sentiment_rating, s.ascore, s.state_id, tg.model_id
FROM states AS s
INNER JOIN topics_geo AS tg ON tg.geo_id = s.state_id AND tg.geo_type = 'state'
)
"""
cursor.execute(query)
connection.commit()
query = """INSERT INTO cities_archive (ascore, sentiment_compound, sentiment_rating, city_id, state_id, model_id)
(SELECT DISTINCT c.ascore, c.sentiment_compound, c.sentiment_rating, c.city_id, c.state_id, tg.model_id
FROM cities AS c
INNER JOIN topics_geo AS tg ON tg.geo_id = c.state_id AND tg.geo_type = 'city'
)
"""
cursor.execute(query)
connection.commit()
cursor.close()
def __init__(self):
self._english = ENGLISH_STOP_WORDS
self._safecen = ENGLISH_STOP_WORDS.union(SAFECEN_STOP_WORDS)
import numpy as np
from gensim.models.word2vec import Word2Vec
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
import spacy
from spacy.en import English, STOP_WORDS
from sklearn.feature_extraction.stop_words import ENGLISH_STOP_WORDS
import string
import re
# load english
parser = English()
# stopwords
stopwords = ENGLISH_STOP_WORDS.union(STOP_WORDS)
# List of symbols we don't care about
symbols = " ".join(
string.punctuation).split(" ") + ["-----", "---", "...", "“", "”", "'ve"]
# part of speech
useful_tags = set([
'ADJ', 'VERB', 'ADV', 'NOUN', 'PROPN', 'PART', 'ADP', 'CONJ', 'INTJ', 'AUX'
])
delimiters = ['. ', '\n', '.\n']
# min words
min_token_length = 2
# set random seed
np.random.seed(111)
# sklearn-compatible mean embedding
def pdf_to_dataset(path, x):
data = []
file_names = np.array([])
meaning = {}
for subdir, dirs, files in os.walk(path):
for file in files:
file_path = subdir + os.path.sep + file
print(file_path)
text = pdf_to_text(file_path)
text = ''.join(ch for ch in text
if ch not in string.punctuation).lower().replace(
'\n', ' ').replace("higher order",
"higherorder")
data.append(text)
file_names = np.append(file_names, file)
#print("sunu okudum: %s" % (file_names))
dataset = sklearn.datasets.base.Bunch(data=data, filenames=file_names)
stop_words = ENGLISH_STOP_WORDS.union([
"al", "et", "set", '±', "document", "term", "used", "based", "?",
"altınel", "7", "62", "2017", "5", "b", "table", "10", "20", "30",
"50", "1", "2", "s", "0", "international", "conference"
])
tf_vectorizer2 = CountVectorizer(tokenizer=LemmaTokenizer(),
stop_words=stop_words,
lowercase=True) # tf
X_data_meaning_tf_corpus = tf_vectorizer2.fit_transform(dataset.data)
freqs_meaning_tf_corpus = {
word: X_data_meaning_tf_corpus.getcol(idx).sum()
for word, idx in tf_vectorizer2.vocabulary_.items()
} # k
termfrequency_corpus = sum(freqs_meaning_tf_corpus.values()) # L
for data in dataset.data:
y = [data]
X_data_meaning_tf_doc = tf_vectorizer2.fit_transform(y)
freqs_meaning_tf_doc = {
word: X_data_meaning_tf_doc.getcol(idx).sum()
for word, idx in tf_vectorizer2.vocabulary_.items()
} # m
termfrequency_doc = sum(freqs_meaning_tf_doc.values()) # B
N = termfrequency_corpus / termfrequency_doc
for word in freqs_meaning_tf_doc.keys():
m = freqs_meaning_tf_doc[word]
k = freqs_meaning_tf_corpus.get(word, 0)
meaning_score = (-1 / m) * (math.log10(combination(k, m))) - (
(m - 1) * math.log10(N))
meaning[word] = meaning_score
sorted_freqs_meaning = sorted(meaning.items(), key=operator.itemgetter(1))
sorted_freqs_meaning = sorted(meaning.items(), key=lambda x: x[1])[:x]
print(sorted_freqs_meaning)
exit()
# term frequency
tf_vectorizer = CountVectorizer(tokenizer=LemmaTokenizer(),
stop_words=stop_words,
lowercase=True,
ngram_range=(2, 2)) # tf
X_data_tf = tf_vectorizer.fit_transform(dataset.data)
# term frequency - inverse document frequency
tfidf_vectorizer = TfidfVectorizer(tokenizer=LemmaTokenizer(),
stop_words=stop_words,
lowercase=True,
ngram_range=(2, 2)) # td-idf
X_data_tfidf = tfidf_vectorizer.fit_transform(dataset.data)
# document frequency
#X_data_df = tfidf_vectorizer.inverse_transform(X=dataset.data)
# x=10
freqs_tf = [(word, X_data_tf.getcol(idx).sum())
for word, idx in tf_vectorizer.vocabulary_.items()]
sorted_freqs_tf = sorted(freqs_tf, key=lambda x: -x[-1])[:x]
print(sorted_freqs_tf) # returns a list
wordcloud_text_tf = [item[0] for item in sorted_freqs_tf]
string_tf = ",".join(wordcloud_text_tf).replace(" ", "_").replace(",", " ")
wordcloud_tf = WordCloud(background_color="white").generate(string_tf)
plt.imshow(wordcloud_tf, interpolation='bilinear')
plt.axis("off")
plt.title("TF")
fig_tf = plt.figure(1)
fig_tf.savefig("tf.png")
freqs_tfidf = [(word, X_data_tfidf.getcol(idx).sum())
for word, idx in tfidf_vectorizer.vocabulary_.items()]
sorted_freqs_tfidf = sorted(freqs_tfidf, key=lambda x: -x[-1])[:x]
print(sorted_freqs_tfidf)
wordcloud_text_tfidf = [item[0] for item in sorted_freqs_tfidf]
string_tfidf = ",".join(wordcloud_text_tfidf).replace(" ", "_").replace(
",", " ")
wordcloud_tfidf = WordCloud(
background_color="white").generate(string_tfidf)
fig_tfidf = plt.figure(2)
plt.imshow(wordcloud_tfidf, interpolation='bilinear')
plt.axis("off")
plt.title("TF-IDF")
fig_tfidf.savefig("tfidf.png")
plt.show()
return
def __init__(self):
self._english = ENGLISH_STOP_WORDS
self._safecen = ENGLISH_STOP_WORDS.union(SAFECEN_STOP_WORDS)
import plotly
import plotly.graph_objs as go
plotly.offline.init_notebook_mode(connected=True)
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.model_selection import train_test_split
#pd.options.display.max_rows = len(df)
import nltk
from sklearn.feature_extraction.stop_words import ENGLISH_STOP_WORDS
nltk.download('punkt')
punc = ['.', ',', '"', "'", '?', '!','+', ':', ';', '(', ')', '[', ']', '{', '}',"%", '-', '✔', '™', '1','2','3','4','5','6','7','8', '9', '0', 'which', 1234567890]
stop_words = ENGLISH_STOP_WORDS.union(punc)
from nltk.tokenize import word_tokenize
from sklearn.feature_extraction.stop_words import ENGLISH_STOP_WORDS
from nltk import word_tokenize,sent_tokenize
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import mean_squared_error
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from imblearn.over_sampling import RandomOverSampler
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import LogisticRegression
from sklearn import ensemble