  Our study employs sentiment analysis to evaluate the compatibility of
Amazon.com reviews with their corresponding ratings. Sentiment analysis is the
task of identifying and classifying the sentiment expressed in a piece of text
as being positive or negative. On e-commerce websites such as Amazon.com,
consumers can submit their reviews along with a specific polarity rating. In
some instances, there is a mismatch between the review and the rating. To
identify the reviews with mismatched ratings we performed sentiment analysis
using deep learning on Amazon.com product review data. Product reviews were
converted to vectors using paragraph vector, which then was used to train a
recurrent neural network with gated recurrent unit. Our model incorporated both
semantic relationship of review text and product information. We also developed
a web service application that predicts the rating score for a submitted review
using the trained model and if there is a mismatch between predicted rating
score and submitted rating score, it provides feedback to the reviewer.
