def get_sentiment(song):
scores = dict([('pos', 0), ('neu', 0), ('neg', 0), ('compound', 0)])
if not song:
return scores
raw_text = song
raw_text = re.sub("\n", ". ", str(raw_text))
# Using already trained
sid = SentimentIntensityAnalyzer()
sentences = tokenize.sent_tokenize(raw_text)
scores = dict([('pos', 0), ('neu', 0), ('neg', 0), ('compound', 0)])
for sentence in sentences:
ss = sid.polarity_scores(sentence)
for k in sorted(ss):
scores[k] += ss[k]
return scores
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