Stanford researchers to open source model they say has nailed sentiment analysis
Here is a link to the live demo: <a href="http://nlp.stanford.edu:8080/sentiment/rntnDemo.html" rel="nofollow">http://nlp.stanford.edu:8080/sentiment/rntnDemo.html</a><p>This is really fun to play with, and I'm surprised how well it can parse the sentiment of sample sentences I threw at it. I've tried a couple random examples (like "I don't know what the artist was smoking, but the song made no sense (though I liked the beat!)") and have not yet gotten a wrong analysis. Even the phrase parsing is pretty spot-on.<p>As a side note, this is much more interesting than the "sediment" analysis I excepted after skimming the title. (Unfortunately though, the analyzer got this final sentence wrong: <a href="http://cl.ly/image/301u1q46263m" rel="nofollow">http://cl.ly/image/301u1q46263m</a>)<p>Edit: seems like this system could get significantly more robust with more data. If you look in the comments section, you can see some comments from the professor himself, i.e. "Possibly because the word "buying", only appears once in the entire dataset and it's in a pretty negative context:
<a href="http://nlp.stanford.edu/sentiment/treebank.html?w=buying"" rel="nofollow">http://nlp.stanford.edu/sentiment/treebank.html?w=buying"</a><p>If you gave it 100,000 phrases, I wouldn't be surprised if it could hit the 95% mark that Socher mentions.