Automatic Ranking of Product Reviews According to Helpfulness
HighlightsA system for sorting through thousands of book reviews to rank the most helpful Identifies helpful information in reviews and automatically ranks them Human evaluators’ choice of most helpful reviews corresponded with the MLLM’s choice 85% of the time Can be used for all types of product reviews, such as consumer electronics Overcomes biases found in user voting mechanisms Our Innovation
A Multi Layer Lexical Model (MLLM)-based algorithm for ranking book reviews. The MMLM approach is a system for data mining and content analysis that examines book reviews in order to establish which of the reviews are the most helpful. If available, the text of the book itself can also be used to enhance the output. The layers contain compact, high-quality lexicons of words specific for each layer, such as terms common in product reviews, specific lexical terms connected with the type of book and terms connected with the title. Key Features
System outperforms voter ranking and random sampling System provides a continuous scale of grading Allows helpful reviews that may potentially be overlooked to be identified Can easily adapt the review ranking to match different criteria, such as review length Fully unsupervised approach precludes the need for human annotations. does not depend on active users– reduces costs Development Milestones
System was developed using books that had large numbers of reviews. Future development will be for a system that works where there are fewer reviews The MLLM approach will be used to generate a single comprehensive review from the reviews ranked most helpful The Opportunity
Can be applied to reviews of all sorts of products to assist consumers make purchasing decisions
Type of Offer: Licensing
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