Python – Sentiment analysis for Twitter in Python

machine-learningnlpopen-sourcepythonsentiment-analysis

I'm looking for an open source implementation, preferably in python, of Textual Sentiment Analysis (http://en.wikipedia.org/wiki/Sentiment_analysis). Is anyone familiar with such open source implementation I can use?

I'm writing an application that searches twitter for some search term, say "youtube", and counts "happy" tweets vs. "sad" tweets.
I'm using Google's appengine, so it's in python. I'd like to be able to classify the returned search results from twitter and I'd like to do that in python.
I haven't been able to find such sentiment analyzer so far, specifically not in python.
Are you familiar with such open source implementation I can use? Preferably this is already in python, but if not, hopefully I can translate it to python.

Note, the texts I'm analyzing are VERY short, they are tweets. So ideally, this classifier is optimized for such short texts.

BTW, twitter does support the ":)" and ":(" operators in search, which aim to do just this, but unfortunately, the classification provided by them isn't that great, so I figured I might give this a try myself.

Thanks!

BTW, an early demo is here and the code I have so far is here and I'd love to opensource it with any interested developer.

Best Solution

Good luck with that.

Sentiment is enormously contextual, and tweeting culture makes the problem worse because you aren't given the context for most tweets. The whole point of twitter is that you can leverage the huge amount of shared "real world" context to pack meaningful communication in a very short message.

If they say the video is bad, does that mean bad, or bad?

A linguistics professor was lecturing to her class one day. "In English," she said, "A double negative forms a positive. In some languages, though, such as Russian, a double negative is still a negative. However, there is no language wherein a double positive can form a negative."

A voice from the back of the room piped up, "Yeah . . .right."