Naive Bayes classifier learning

This is a method discussed in Social Media Analysis 10-802 in Spring 2010 and Social Media Analysis 10-802 in Spring 2011.

Background

A Naive Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem (from Bayesian statistics) with strong (naive) independence assumptions.

Here, we use an example from sentiment analysis on twitter messages. So we let s be the sentiment label, M be the Twitter message. If we assume there are equal sets of positive, negative and neutral messages, we simplify the equation:

If we re-write M into G a set of features and assume they are conditionally independent, we have:

Finally, we have the log-likelihood of each sentiment:

Applications

It is widely used in information retrieval and information extraction, for example, Document Categorization, Text Classification and many different problems.

Relevant Papers

	AddressesProblem	UsesDataset
Pang et al EMNLP 2002	Review classification	Pang Movie Reviews