Arpan-Mishra / Amazon-Fine-Food-Reviews-

Using KNN and Bag of Words to predict the polarity of Reviews

Amazon-Fine-Food-Reviews-

Using KNN and Bag of Words to predict the polarity of Reviews

• The Amazon Fine Food Reviews dataset consists of reviews of fine foods from Amazon.
• Number of reviews: 568,454 Number of users: 256,059 Number of products: 74,258 Timespan: Oct 1999 - Oct 2012 Number of Attributes/Columns in data: 10

Attribute Information:

1. Id
2. ProductId - unique identifier for the product
3. UserId - unqiue identifier for the user
4. ProfileName
5. HelpfulnessNumerator - number of users who found the review helpful
6. HelpfulnessDenominator - number of users who indicated whether they found the review helpful or not
7. Score - rating between 1 and 5
8. Time - timestamp for the review
9. Summary - brief summary of the review
10. Text - text of the review

Objective:

Given a review, determine whether the review is positive (rating of 4 or 5) or negative (rating of 1 or 2).

Note: I have taken only 5000 reviews for this purpose because of computational constraints.

Checkout the notebook for full end to end implementation with proper expplanation.