def pronouns_1(Authors):
for author in Authors:
pronouns = 0
for tweet in author['tweets']:
current_preprocessed_tweet = clean_es.tokenize(tweet)
pronouns = pronouns + current_preprocessed_tweet.count('<pronoun>')
author['pronouns'] = pronouns
def get_all_specific_features(Authors):
features = []
for author in Authors:
features_user = []
emoji = 0
first_person = 0
pronouns = 0
negations = 0
for tweet in author['tweets']:
current_preprocessed_tweet = clean_es.tokenize(tweet)
emoji = emoji + emojis.count(tweet)
first_person = first_person + \
current_preprocessed_tweet.count('<first_person>')
pronouns = pronouns + current_preprocessed_tweet.count('<pronoun>')
negations = negations + \
current_preprocessed_tweet.count('<negation>')
features_user.append(emoji)
features_user.append(first_person)
features_user.append(pronouns)
features_user.append(negations)
features.append(features_user)
return features
def negations_1(Authors):
for author in Authors:
negations = 0
for tweet in author['tweets']:
current_preprocessed_tweet = clean_es.tokenize(tweet)
negations = negations + \
current_preprocessed_tweet.count('<negation>')
author['negations'] = negations
def first_person_1(Authors):
for author in Authors:
first_person = 0
for tweet in author['tweets']:
current_preprocessed_tweet = clean_es.tokenize(tweet)
first_person = first_person + \
current_preprocessed_tweet.count('<first_person>')
author['first_person'] = first_person
def pronouns_2(Authors):
features = []
for author in Authors:
pronouns = 0
for tweet in author['tweets']:
current_preprocessed_tweet = clean_es.tokenize(tweet)
pronouns = pronouns + current_preprocessed_tweet.count('<pronoun>')
features.append(pronouns)
return features
def negations_2(Authors):
features = []
for author in Authors:
negations = 0
for tweet in author['tweets']:
current_preprocessed_tweet = clean_es.tokenize(tweet)
negations = negations + \
current_preprocessed_tweet.count('<negation>')
features.append(negations)
return features
def first_person_2(Authors):
features = []
for author in Authors:
first_person = 0
for tweet in author['tweets']:
current_preprocessed_tweet = clean_es.tokenize(tweet)
first_person = first_person + \
current_preprocessed_tweet.count('<first_person>')
features.append(first_person)
return features