You are applying for a position at AWS and your interviewer wants to know more about your sentiment classification skills on products sold by amazon.com

All reviews are classified by a labeling subcontractor (e.g. AWS Mechanical Turk) as having positive ( = 1) or negative ( = 0) sentiment. The interviewer asks you to explain all the steps of designing a sentiment classifier based on Logistic Regression.

The interviewer was impressed with your answers up to now but to make your life difficult (its AWS after all) asks you how is Logistic Regression different than a method you have never seen before: Naive Bayes. You are given this wikipedia page https://en.wikipedia.org/wiki/Naive_Bayes_classifier and you are told to focus your attention to the posterior probability differences between the two models. NOTE: Write down the posterior in the log domain to make it amendable to implementation (next question). Also if you copy the wikipedia answer you will be granted exactly 0 points