vaderSentiment seems to underweight long sentences, not useful at detecting sarcasm, and can't interpret messages right after a hashtag.

[neldivad]

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.

2. 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}

3. 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]

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]

Might want to try to weight sentiment based on sentence length