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Yelp Sentiment Classification
https://github.com/weakrules/Denoise-multi-weak-sources/tree/master/rules-noisy-labels/Yelp
# Labels
"0": "Negative",
"1": "Positive"
# Labeling functions
lfs = [
textblob_lf,
keyword_recommend,
keyword_general,
keyword_mood,
keyword_service,
keyword_price,
keyword_environment,
keyword_food,
]
# lf - textblob_lf
@preprocessor(memoize=True)
def textblob_sentiment(x):
scores = TextBlob(x.text)
x.polarity = scores.sentiment.polarity
x.subjectivity = scores.sentiment.subjectivity
return x
@labeling_function(pre=[textblob_sentiment])
def textblob_lf(x):
if x.polarity < -0.5:
return NEG
if x.polarity > 0.5:
return POS
return ABSTAIN
# lf - keyword_recommend
keyword_recommend = make_keyword_lf(name="keyword_recommend",
keywords_pos=["recommend"],
keywords_neg=[])
# lf - keyword_general
keyword_general = make_keyword_lf(name="keyword_general",
keywords_pos=["outstanding", "perfect", "great", "good", "nice", "best", "excellent", "worthy", "awesome", "enjoy", "positive", "pleasant", "wonderful", "amazing"],
keywords_neg=["bad", "worst", "horrible", "awful", "terrible", "nasty", "shit", "distasteful", "dreadful", "negative"])
# lf - keyword_mood
keyword_mood = make_keyword_lf(name="keyword_mood",
keywords_pos=["happy", "pleased", "delighted", "contented", "glad", "thankful", "satisfied"],
keywords_neg=["sad", "annoy", "disappointed", "frustrated", "upset", "irritated", "harassed", "angry", "pissed"])
# lf - keyword_service
keyword_service = make_keyword_lf(name="keyword_service",
keywords_pos=["friendly", "patient", "considerate", "enthusiastic", "attentive", "thoughtful", "kind", "caring", "helpful", "polite", "efficient", "prompt"],
keywords_neg=["slow", "offended", "rude", "indifferent", "arrogant"])
# lf - keyword_price
keyword_price = make_keyword_lf(name="keyword_price",
keywords_pos=["cheap", "reasonable", "inexpensive", "economical"],
keywords_neg=["overpriced", "expensive", "costly", "high-priced"])
# lf - keyword_environment
keyword_environment = make_keyword_lf(name="keyword_environment",
keywords_pos=["clean", "neat", "quiet", "comfortable", "convenien", "tidy", "orderly", "cosy", "homely"],
keywords_neg=["noisy", "mess", "chaos", "dirty", "foul"])
# lf - keyword_food
keyword_food = make_keyword_lf(name="keyword_food",
keywords_pos=["tasty", "yummy", "delicious", "appetizing", "good-tasting", "delectable", "savoury", "luscious", "palatable"],
keywords_neg=["disgusting", "gross", "insipid"])
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