2. Naive Bayes: Given the following documents with counts for the sentiment vocabulary V ={good, poor, great, terrible}, and positive or negative class labels: Document D D2 + D3 "good" poor" "great" "terrible Class Label 1 1 2 0 0 0 0 1 1 2 0 1 1 3 2 2 3 0 2 0 + 0 0 0 1 D4 D5 D6 This assignment is adapted from course material by Drs. Chen and Narasimhan at Princeton University. (a) Provide all the relevant probabilities for a naive Bayes model with add-1 smoothing, and assign a class label to the following test sentence: great characters and good acting, but terrible plot (b) Often whether or not a word occurs is more important than the frequency. A variant of naive Bayes, called binarized naive Bayes, clips the word counts in each document at 1. Compute a binarized naive Bayes model with add-1 smoothing on the same documents and predict the label of the same test sentence as before. Do the two models agree? Which model do you prefer, and why