Sentiment analysis for Twitter in Python [closed]

Question

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.

Durrett

Answer 1

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.

Inhuman

Thanks. I'm only doing this at nights, so... it'll take some time, but I'll post an update when I have something ready – Durrett

NLTK code is available under Apache License 2.0 as per nltk.org/faq – Invert

Why do you say Weka is for pedagogical purposes? Isn't it part of pentaho BI suite? And pentaho does serve enterprises. – Dextrorse

Answer 2

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."

Harryharsh

I'm glad you had a useful answer, as I otherwise might have had to upvote you just for that quote. :-) – Dallis

I think the quote was "yeah yeah" - from Sidney Morgenbesser – Hubbs

Answer 3

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!

Durrett

blog post link is not working anymore, could you update it? – Unweave

Hi @PetrutiuMihai indeed that blog was taken down. But it's pretty old stuff, not at the front of research as of today, so you won't be missing much ;( – Durrett

Answer 4

I have constructed a word list labeled with sentiment. You can access it from here:

http://www2.compute.dtu.dk/pubdb/views/edoc_download.php/6010/zip/imm6010.zip

You will find a short Python program on my blog:

http://finnaarupnielsen.wordpress.com/2011/06/20/simplest-sentiment-analysis-in-python-with-af/

This post displays how to use the word list with single sentences as well as with Twitter.

Word lists approaches have their limitations. You will find a investigation of the limitations of my word list in the article "A new ANEW: Evaluation of a word list for sentiment analysis in microblogs". That article is available from my homepage.

Please note a unicode(s, 'utf-8') is missing from the code (for paedagogic reasons).

Vichyssoise

"Posterous Spaces is no longer available" Could you post python code somewhere? – Butta

Thanks for noting it. I have now changed the posterous link to a Wordpress link where I moved my blog. – Kaylil

Could you say something about any experiments with your sentiment wordslit? I mean what was precission, recall of classification. – Butta

I have links to a few evaluations here: neuro.compute.dtu.dk/wiki/AFINN#Evaluation I have not myself evaluated its performance in terms of precision, recall and classification. What I did was rank correlation with Mislove's Amazon Mechanical Turk labeling of tweets. – Kaylil

Answer 5

A lot of research papers indicate that a good starting point for sentiment analysis is looking at adjectives, e.g., are they positive adjectives or negative adjectives. For a short block of text this is pretty much your only option... There are papers that look at entire documents, or sentence level analysis, but as you say tweets are quite short... There is no real magic approach to understanding the sentiment of a sentence, so I think your best bet would be hunting down one of these research papers and trying to get their data-set of positively/negatively oriented adjectives.

Now, this having been said, sentiment is domain specific, and you might find it difficult to get a high-level of accuracy with a general purpose data-set.

Good luck.

Tabulate

Darn, I was going to preempt you. – Brinker

Answer 6

I think you may find it difficult to find what you're after. The closest thing that I know of is LingPipe, which has some sentiment analysis functionality and is available under a limited kind of open-source licence, but is written in Java.

Also, sentiment analysis systems are usually developed by training a system on product/movie review data which is significantly different from the average tweet. They are going to be optimised for text with several sentences, all about the same topic. I suspect you would do better coming up with a rule-based system yourself, perhaps based on a lexicon of sentiment terms like the one the University of Pittsburgh provide.

Check out We Feel Fine for an implementation of similar idea with a really beautiful interface (and twitrratr).

Bloodshed

Answer 7

Take a look at Twitter sentiment analysis tool. It's written in python, and it uses Naive Bayes classifier with semi-supervised machine learning. The source can be found here.

Peeper

Answer 8

I came across Natural Language Toolkit a while ago. You could probably use it as a starting point. It also has a lot of modules and addons, so maybe they already have something similar.

Torhert

Answer 9

Maybe TextBlob (based on NLTK and pattern) is the right sentiment analysis tool for you.

Ivatts

Answer 10

Somewhat wacky thought: you could try using the Twitter API to download a large set of tweets, and then classifying a subset of that set using emoticons: one positive group for ":)", ":]", ":D", etc, and another negative group with ":(", etc.

Once you have that crude classification, you could search for more clues with frequency or ngram analysis or something along those lines.

It may seem silly, but serious research has been done on this (search for "sentiment analysis" and emoticon). Worth a look.

Couple

Answer 11

There's a Twitter Sentiment API by TweetFeel that does advanced linguistic analysis of tweets, and can retrieve positive/negative tweets. See http://www.webservius.com/corp/docs/tweetfeel_sentiment.htm

Prolix

Answer 12

For those interested in coding Twitter Sentiment Analyis from scratch, there is a Coursera course "Data Science" with python code on GitHub (as part of assignment 1 - link). The sentiments are part of the AFINN-111.

You can find working solutions, for example here. In addition to the AFINN-111 sentiment list, there is a simple implementation of builing a dynamic term list based on frequency of terms in tweets that have a pos/neg score (see here).

Starter