Unsupervised Topic Extraction for the Reviewer’s Assistant
User generated reviews are now a familiar and valuable part of most ecommerce sites since high quality reviews are known to influence purchasing decisions. In this paper we describe work on the Reviewer’s Assistant (RA), which is a recommendation system that is designed to help users to write better reviews. It does this by suggesting relevant topics that they may wish to discuss based on the product they are reviewing and the content of their review so far. We build on prior work and describe an unsupervised topic extraction module for the RA system that enhances the system’s ability to automatically adapt to new content categories and application domains. Our main contribution includes the results of a controlled, live-user study to show that the RA system is capable of supporting users to create reviews that enjoy higher quality ratings than Amazon’s own high quality reviews, even without using manually created topic models.
KeywordsAssociation Rule Latent Dirichlet Allocation Online Review Product Review Review Quality
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