Other Question
We want to build a naive Bayes sentiment classifier using add-1 smoothing. Here is our training set:
– the movie has no plot
– honestly pretty boring
+ pretty interesting movie
Test Set:
pretty enjoyable plot
Answer the questions below given the sets above:
Compute the prior for the two classes + and -, and the likelihoods for each word given the class (leave in the form of fractions).
Would using binary multinomial Naive Bayes change anything?
- Why do you add |V| to the denominator of add-1 smoothing, instead of just counting the words in one class?
- Watch the following videos in the lecture series to prepare you for this Assignment:
- 3.1 Intoduction to N-grams (time 8:42)
3.5 Smoothing: Add-One (time 6:31)
https://www.youtube.com/playlist?list=PLoROMvodv4r…
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