52-technologies-in-2016
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shekhargulati
Feedback: Week 3 Stanford CoreNLP blog
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Great post. What if we could easily tell if any article is positive or negative? I love the idea of adding sentiment to articles so you could easily tell if its first, highly emotional/opinionated and second, what that emotional bias is. I can see Facebook adding sentiment analysis to the news feed.
Interesting post, I wouldn't say great compared to the other reading content you put up, but I appreciate the time. I think what would have made it more valuable is if you dove into the Stanford NLP framework itself a little more. Explaining the annotations, showing some of the feedback and values you get back, etc.
Definitely a solid primer, however.
@tylerjharden actually I was working on slick post that day but blog was not shaping up well. So, I decided to write about Stanford CoreNLP with few hours left in the day. I agree I didn't covered Stanford CoreNLP in detail. Will definitely clean it up and make it better. Thanks for honest feedback. Also, I would be more than happy to accept PR if you are willing to add few sections and details :). Thanks Again.
@shekhargulati If I knew anything about it, I'd be more than happy to. If I get the time I will gladly contribute, I like your model here.
Interestingly changing "Dhoni" to "John" changes the sentiment from NEGATIVE to NEUTRAL The sentence in the test: "Dhoni laments bowling, fielding errors in series loss" I'm not a native english speaker so perhaps I'm missing something here ...
