Simple Sentiment Analysis

Question

It appears that the simplest, naivest way to do basic sentiment analysis is with a Bayesian classifier (confirmed by what I'm finding here on SO). Any counter-arguments or other suggestions?

Answer 1

A Bayesian classifier with a bag of words representation is the simplest statistical method. You can get significantly better results by moving to more advanced classifiers and feature representation, at the cost of more complexity.

Statistical methods aren't the only game in town. Rule based methods that have more understanding of the structure of the text are the other main option. From what I have seen, these don't actually perform as well as statistical methods.

I recommend Manning and Schütze's Foundations of Statistical Natural Language Processing chapter 16, Text Categorization.

Answer 2

I can't think of a simpler, more naive way to do Sentiment Analysis, but you might consider using a Support Vector Machine instead of Naive Bayes (in some machine learning toolkits, this can be a drop-in replacement). Have a look at "Thumbs up? Sentiment Classification using Machine Learning Techniques" by Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan which was one of the earliest papers on these techniques, and gives a good table of accuracy results on a family of related techniques, none of which are any more complicated (from a client perspective) than any of the others.

Answer 3

Building upon the answer provided by Ken above, there is another paper

"Sentiment analysis using support vector machines with diverse information sources" by Tony and Niger,

which looks at assigning more features than just a bag of words used by Pang and Lee. Here, they leverage wordnet to determine semantic differentiation of adjectives, and proximity of the sentiment towards the topic in the text, as additional features for SVM. They show better results than previous attempts to classify text based on sentiment.