Naive Bayes classifier learning
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:
It is widely used in information retrieval and information extraction, for example, Document Categorization, Text Classification and many different problems.
|Pang et al EMNLP 2002||Review classification||Pang Movie Reviews|