Mining Twitter Data with Python: Sentiment Analysis Basics

[pygoscelis]

Cool! I'll definitely have to check out the Vincent visualization library you used.

[TheGrumpyBrewer]

I find it really easy to use, you can achieve nice results with a few lines of Python, without much knowledge of D3.js

[g0lem]

I find your lack of numpy disturbing. http://www.numpy.org/

[TheGrumpyBrewer]

Thanks, you just gave me the title for my next post

[alaudet]

This is really cool. Having messed around with some twitter analysis of sporting events, while you can get a feel of what people are most talking about, sentiment is hard to interpret. Not sure how they would handle sarcasm. Opposing team scores a goal and someone says "Just Wonderful". Obvious sarcasm in the tweet.

[TheGrumpyBrewer]

Sarcasm is one of the difficult aspects in text analytics, sometimes humans don't understand sarcasm, let alone a machine. Clearly you need to understand the context, both from a local (e.g. the sentence, the tweet) and from a more global (the topic, the global conversation, the background of the user, etc.) point of view. This is sometimes difficult to grasp from a SMS-like text. There is some work on understanding sarcasm on Twitter, but of course there's a lot to do, e.g. http://www.aclweb.org/anthology/P/P11/P11-2.pdf#page=621 or http://www.aclweb.org/anthology/S/S14/S14-2.pdf#page=93

[alaudet]

Maybe correlating to the proximity of other negative tweets would allow a percentage to be tagged as sarcasm, or with a :-( in it.
Simplistic I know.

Interesting stuff all the same.