Feedback

Thank you for the feedback!

• "Movie was not !good" -> "Movie was not !good" I am not removing the not as of now. Since the process occurs only once it is not going to be recursive. If time permits I will try to remove the not and test it out. The token "!good" gets stored in our Bayes classifer as having appeared in a negative review.
• Yes the model assigns 50% (completely uncertain) to any token that is not seen in the training dataset. I will mention it in the page.
• For the reviews I correlated number of stars with positive sentiment. I did paste of the reviews on the page. But most of them are too long and made the page look weird.
• Thank you for finding the culprit for the Fast and Furious review!
• If time permits I will try to clean up the dataset.
• Yes I did random sorting on the training data leading to different probabilities. I will mention it in the page!