Deep Learning Sentiment Analysis of Amazon.com Reviews and Ratings
This addresses data quality issues for e-commerce platforms and consumers, but it is incremental as it applies existing methods to a specific domain.
The study tackled the problem of mismatched sentiment between Amazon.com reviews and their ratings by using deep learning sentiment analysis, achieving a model that predicts rating scores and identifies inconsistencies.
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.