Identifying the Relative Importance of Customer Issues on Product Ratings through Machine Learning

Self-attention based review sentence identification followed by clustering and issue importance ranking

Abstract

Millions of customer reviews for products are available online across hundreds of different websites. These reviews have a tremendous influence on the purchase decision of new customers and in creating a positive brand image. Understanding which of the product issues are critical in determining the product ratings is crucial for marketing teams. We have developed a solution which can derive deep insights from customer reviews which goes significantly beyond keyword based analysis. Our solution can identify key customer issues voiced in the reviews and the impact of each of these on the final rating that a customer gives the product. This insight is very actionable as it helps identify which customer concerns are responsible for bad ratings of products.

Publication
In Proceedings of the ACM Symposium on Document Engineering 2018
Md Imbesat Hassan Rizvi
Md Imbesat Hassan Rizvi
Technical (Research) Associate

My research interests include scientific machine learning, natural language processing, reinforcement learning, robotics and human-robot interaction.