We want to build a naive bayes sentiment classifier using add-1 smoothing, as described in the lecture (not binary naive bayes, regular naive bayes). Here is our training corpus:
Training Set:
- just plain boring - entirely predictable and lacks energy - no surprises and very few laughs + very powerful + the most fun film of the summer
Test Set:
predictable with no originality
Compute the prior for the two classes + and -, and the likelihoods for each word given the class
(leave in the form of fractions).
Then compute whether the sentence in the test set is of class positive or negative
(you may need a computer for this final computation). Make sure you know the correct
the Bayes equation to use to compute a value for each class in order to answer this question.
What would the answer be without add-1 smoothing?
(Optional: would using binary multinomial Naive Bayes change anything?)
Part 2: Challenge Problems