def get_Emoji_sentiment(self, lemma):
lexeme_sentiment = {}
unicode_emoji = Emoji.ascii_to_unicode(lemma.upper())
if len(unicode_emoji) == 1:
category = unicodedata.category(unicode_emoji)
if category == 'So': # is "Symbol other" (a specific unicode category)
shortcode = Emoji.unicode_to_shortcode(unicode_emoji)
if shortcode != unicode_emoji: # is an emoji only if it has an emoji shortcode
# check whether the emoji is in the emoji polarity lexicon
if unicode_emoji in self.emoji: # is in the lexicon
emodict = self.emoji[unicode_emoji]
lexeme_sentiment["0"] = { "shortcode":shortcode, "negativity":emodict["negativity"], "positivity":emodict["positivity"] }
else: # tokenize the shortcode and get its tokens polarities from SentiWordNet
tokens = shortcode.strip(' :').split('_') # shortcode tokenization seems an easy problem to solve
negativity = 0
positivity = 0
count = 0 # count the number of shortcode tokens with a synset
for token in tokens:
synsets = list(swn.senti_synsets(token))
if len(synsets)>0:
senti_synset = synsets[0]
negativity += senti_synset.neg_score()
positivity += senti_synset.pos_score()
count += 1
if count > 1: # take the average of all shortcode tokens polarities
negativity /= count
positivity /= count
lexeme_sentiment["0"] = { "shortcode":shortcode, "negativity":negativity, "positivity":positivity }
# print(lexeme_sentiment["0"])
return lexeme_sentiment
def action_from_answer(self, user, msg, story_model=Story):
answers = self.get_prev_answers(user, story_model=story_model)
if answers:
text = Emoji.unicode_to_shortcode(self.read_user_message(msg))
for f in answers:
if f.text == text:
return f.action
return None
def action_from_answer(self, user, msg):
s = Story.objects.filter(bulletin=user.current_bulletin,
order=user.current_story_order).first()
if s is None:
return
f = Fragment.objects.filter(story=s,
text=Emoji.unicode_to_shortcode(
msg['text'])).first()
if f is None:
return
return f.action
