Deep Learning Sentiment Analysis of Amazon.Com Reviews and Ratings

Nishit Shrestha, Fatma Nasoz
<span title="2019-02-28">2019</span> <i title="Academy and Industry Research Collaboration Center (AIRCC)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qty27capxvbkfojct55lrrgazm" style="color: black;">International Journal on Soft Computing Artificial Intelligence and Applications</a> </i> &nbsp;
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
more &raquo; ... 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5121/ijscai.2019.8101">doi:10.5121/ijscai.2019.8101</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/f7v2ewyf25e4leu5jmxspjs4n4">fatcat:f7v2ewyf25e4leu5jmxspjs4n4</a> </span>
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