Machine Learning and Deep Learning Models for Sentiment Analysis of Product Reviews

Abstract

In our research, we use sentiment analysis to determine how well ratings and reviews are compared on Amazon.com. The process of determining whether a text's tone is favorable or negative and labelling it as such is known as sentiment analysis. Consumers may write evaluations on e-commerce sites like Amazon.com and indicate the polarity of their opinion. There is a discrepancy between the review and the rating in certain cases. We used deep learning to analyze the sentiment of Amazon.com product reviews in order to find reviews with inconsistent star ratings. A paragraph vector was utilized to transform textual product evaluations into numeric data that was then fed into a neural network with recurrent equipped with a gated recurrent unit for training. We built a model that takes into account the review text's semantic connections to the product data. Additionally, we built a web service that uses the trained model to predict the rating score of a submitted review and gives feedback to a reviewer if the anticipated and submitted ratings do not line up.