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:
Id
ProductId - unique identifier for the product
UserId - unqiue identifier for the user
ProfileName
HelpfulnessNumerator - number of users who found the review helpful
HelpfulnessDenominator - number of users who indicated whether they found the review helpful or not
Score - rating between 1 and 5
Time - timestamp for the review
Summary - brief summary of the review
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)
With the perception of text/review we predicted the polarity of review.In this project we applied various algorithm such as KNN,Naive Bayes,Logistic Regression,Support Vector machine,Decision trees,Random forest & GBDT ,LSTM .