# Deep Learning Sentiment Analysis of Amazon.com Reviews and Ratings

arXiv: https://arxiv.org/abs/1904.04096 · HTML: https://mlsanity.com/paper/1904.04096/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [LaDiff ULMFiT: A Layer Differentiated training approach for ULMFiT](https://mlsanity.com/paper/2101.04965/) (2021)
- [Technologies for AI-Driven Fashion Social Networking Service with E-Commerce](https://mlsanity.com/paper/2203.10996/) (2022)
- [textitGeoHard: Towards Measuring Class-wise Hardness through Modelling Class Semantics](https://mlsanity.com/paper/2407.12512/) (2024)

## Abstract (arXiv)

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.