Student Research

Aspect-based Opinion Summarization: A Case Study with Amazon Product Reviews

With the rapid advancement of eCommerce, it has become a common trend for customers to write reviews about any product they purchase. For certain popular products, such as cell phones, laptops, tablets and so on, the number of reviews can be hundreds or even thousands, making it difficult for potential customers to identify specific aspect based overview of the product (for example, screen, camera, battery etc) of their interest. In this paper, we present a framework that generates short opinionated-summaries of customer reviews for each aspects identified from customer reviews. We have proposed an enhanced Naive Bayes classifier for automatic aspect identification from customer reviews, later we used SubSum, a subjective logic framework for extractive summarization to generate short summaries after predicting the sentiment polarities of the customers on each aspect of the product reviews.


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