def get_subjectivity_score_tr(user):
# Gets polarity score for TR tweets
# -100 is the negative end, 100 is the positive end
tr = Translator()
tweets = get_tweets(user)
tweets_text = tweets_to_string(tweets)
tr_text = tr.translate(tweets_text).text
return int(TextBlob(tr_text).sentiment.subjectivity * 100)
def get_gender_pred_tr(user):
# We only have male and female for the turkish model
model_path = f"{URL_ROOT}models/tr/gender_model_tr.pkl"
model = pickle.load(open(model_path, "rb"))
tweets = tweets_to_string(get_tweets(user))
gender_pred = model.predict_proba((pd.Series(tweets)))
male_pred = gender_pred[:, 0]
male_score = int(male_pred * 100)
female_score = 100 - male_score
return {"male": male_score, "female": female_score}
def get_gender_pred_en(user):
model_path = f"{URL_ROOT}models/en/gender_model_en.pkl"
model = pickle.load(open(model_path, "rb"))
tweets = tweets_to_string(get_tweets(user))
gender_pred = model.predict_proba((pd.Series(tweets)))
male_pred = gender_pred[:, 0]
brand_pred = gender_pred[:, 2]
male_score = int(male_pred * 100)
brand_score = int(brand_pred * 100)
female_score = 100 - male_score - brand_score
return {"male": male_score, "female": female_score, "brand": brand_score}
def create_wordclouds(user):
# Returns the path for wordclouds for tweets and mentioned
path_tw = f"{URL_ROOT}static/images/{user.username}_tw_wordcloud.png"
path_ment = f"{URL_ROOT}static/images/{user.username}_ment_wordcloud.png"
path_stopwords_tr = f"{URL_ROOT}models/datasets/stopwords-tr.txt"
stopwords_tr = txt_to_list(path_stopwords_tr) \
+ ["bi", "var", "yok", "sadece", "bence", "sence", "bi", "evet", "hayır", "peki", "tamam",
"başka", "aynı", "lazım", "yav", "lan", "la", "olm"]
stopwords_tr_en = list(STOPWORDS) + stopwords_tr
if os.path.isfile(path_tw):
os.remove(path_tw)
if os.path.isfile(path_ment):
os.remove(path_ment)
tw_str = tweets_to_string(get_tweets(user))
ment_str = tweets_to_string(get_mentioned(user))
cloud_tw = WordCloud(stopwords=set(stopwords_tr_en))
cloud_tw.generate(tw_str)
cloud_tw.to_file(path_tw)
ment_tw = WordCloud(stopwords=set(stopwords_tr_en))
ment_tw.generate(ment_str)
ment_tw.to_file(path_ment)
return {"tweet_wordcloud": path_tw, "ment_wordcloud": path_ment}
def get_subjectivity_score_tr(user):
# Gets subjectivity score for TR tweets
# -100 is the negative end, 100 is the positive end
tr = Translator()
score = 0
tweets = get_tweets(user)
tweets_text = tweets_to_string(tweets)
text_list = []
for i in range(0, len(tweets_text), 15000):
text_list.append(tweets_text[i:i + 15000])
translations = tr.translate(text_list)
for translation in translations:
score += int(TextBlob(translation.text).sentiment.subjectivity * 100)
score = int(score / len(text_list))
return score
def get_subjectivity_score_en(user):
# Gets subjectivity score for EN tweets
# 0 is the factual end, 100 is the subjective end
tweets = get_tweets(user)
tweets_text = tweets_to_string(tweets)
return int(TextBlob(tweets_text).sentiment.subjectivity * 100)
def get_polarity_score_en(user):
# Gets polarity score (is the tweet negative, positive or neutral?) for EN tweets
# -100 is the negative end, 100 is the positive end
tweets = get_tweets(user)
tweets_text = tweets_to_string(tweets)
return int(TextBlob(tweets_text).sentiment.polarity * 100)