Multinomial Naive Bayes

Naive Bayes has three flaws when applied to document classification. First, a word’s non-appearance counts just as much its appearance, whereas surely a document’s class is determined by the words that are in it rather than those that aren’t? Second, Naive Bayes doesn’t take account of the number of appearances of a word, whereas surely frequently occurring words should have a greater influence on the class than ones that only appear once? Third, it treats all words the same, whereas surely unusual words like “weka” and “breakfast” should count more than common ones like “and” and “the”? Multinomial Naive Bayes is a classification method that solves these problems and is generally better and faster than plain Naive Bayes.

(Note: Ian sets “stopList” to “True” in this video. In the version of Weka you are using you should set “stopwordsHandler” to “Rainbow”.)