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cjhutto / vaderSentiment

VADER Sentiment Analysis. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.
MIT License
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vaderSentiment seems to underweight long sentences, not useful at detecting sarcasm, and can't interpret messages right after a hashtag. #120

Open neldivad opened 3 years ago

neldivad commented 3 years ago

I've tried vaderSentiment on tweets about the topic "Amber Heard". In a sample size of 100, all of the tweets are negative towards the topic. Here are some issues I've encountered:

  1. vader is bad at detecting sarcasm.

Sarcastic memes with a caption such as "Thing's that hit different: Amber Heard", "The Gorgeous Ad of Amber Heard" are falsely labeled as incredibly positive.

  1. vader does not understand words suffixed by hashtags.

analyzer.polarity_scores('You all speak about #ToxicMasculinity but where is #ToxicFeminity? #AmberHeard') {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}

  1. vader sometimes yield questionable results.

analyzer.polarity_scores('God I can\'t stand her face') >>> {'neg': 0.0, 'neu': 0.704, 'pos': 0.296, 'compound': 0.2732}

PeterH220 commented 3 years ago

Do you really expect detection of Amber Heard? wtf get real. Even 97% of people don't have a clue who the hell she is.

internot169 commented 1 year ago

Might want to try to weight sentiment based on sentence length