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Clicked through ~20 of them and the analysis was completely off in most cases.
by aw3c2 12y ago
Clicked through ~20 of them and the analysis was completely off in most cases.
- robin_reala 12y agoYep, although in some cases understandably. One tweet listed as ‘Not good’ had the text ‘Killed it! 🔫’ and a location given as a comedy club. I’m assuming someone had had a good gig, but I’m not surprised a classifier algorithm got that wrong.
- _delirium 12y agoSome cases are just difficult, but the overall accuracy could probably be improved considerably if the sentiment analyzer were calibrated for the domain. AFINN (linked above) is calibrated from newspaper articles, which almost certainly have a different distribution of word/sentiment correlations than Tweets do. It's not hard for me to imagine that "killed" is a better predictor of negative sentiment when classifying news articles.
- MrMattWright 12y agoSentiment.js (which is what we used in devwax and I am guessing the same here) is just AFINN based Sentiment.https://www.npmjs.org/package/sentiment https://www.npmjs.org/package/sentiment which you can customize. So in our case we added things like {"barreling": 2} etc. What would be better is a bigram/trigram based approach do you could score "Going Off" and "Killed It" etc, but I am not sure if there is a js library that does that?
- chatmasta 12y agoMakes sense, since AFINN is just a tab-separated list of words and [-5, 5] valence scores. [1] The impressive part of this for me is the visualization. Really nice. http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6010 http://www2.imm.dtu.dk/pubdb/views/publication_details.php?i...