Deep Learning Sentiment Analysis of Amazon.com Reviews and Ratings

TL;DR

This paper uses deep learning-based sentiment analysis to evaluate and detect mismatches between Amazon reviews and their ratings, improving review authenticity verification.

Contribution

It introduces a novel deep learning approach combining paragraph vectors and RNNs to assess review-rating consistency on Amazon.

Findings

Model accurately detects mismatched reviews

Web service provides real-time feedback to reviewers

Enhanced review authenticity verification

Abstract

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.