Sentiment analysis for Twitter in Python [closed]

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."

With most of these kinds of applications, you'll have to roll much of your own code for a statistical classification task. As Lucka suggested, NLTK is the perfect tool for natural language manipulation in Python, so long as your goal doesn't interfere with the non commercial nature of its license. However, I would suggest other software packages for modeling. I haven't found many strong advanced machine learning models available for Python, so I'm going to suggest some standalone binaries that easily cooperate with it.

You may be interested in The Toolkit for Advanced Discriminative Modeling, which can be easily interfaced with Python. This has been used for classification tasks in various areas of natural language processing. You also have a pick of a number of different models. I'd suggest starting with Maximum Entropy classification so long as you're already familiar with implementing a Naive Bayes classifier. If not, you may want to look into it and code one up to really get a decent understanding of statistical classification as a machine learning task.

The University of Texas at Austin computational linguistics groups have held classes where most of the projects coming out of them have used this great tool. You can look at the course page for Computational Linguistics II to get an idea of how to make it work and what previous applications it has served.

Another great tool which works in the same vein is Mallet. The difference between Mallet is that there's a bit more documentation and some more models available, such as decision trees, and it's in Java, which, in my opinion, makes it a little slower. Weka is a whole suite of different machine learning models in one big package that includes some graphical stuff, but it's really mostly meant for pedagogical purposes, and isn't really something I'd put into production.

Good luck with your task. The real difficult part will probably be the amount of knowledge engineering required up front for you to classify the 'seed set' off of which your model will learn. It needs to be pretty sizeable, depending on whether you're doing binary classification (happy vs sad) or a whole range of emotions (which will require even more). Make sure to hold out some of this engineered data for testing, or run some tenfold or remove-one tests to make sure you're actually doing a good job predicting before you put it out there. And most of all, have fun! This is the best part of NLP and AI, in my opinion.

Thanks everyone for your suggestions, they were indeed very useful!I ended up using a Naive Bayesian classifier, which I borrowed from here. I started by feeding it with a list of good/bad keywords and then added a "learn" feature by employing user feedback. It turned out to work pretty nice.

The full details of my work as in a blog post.

Again, your help was very useful, so thank you!