Abstract
With product reviews growing in depth and becoming more numerous, it is growing challenge to acquire a comprehensive understanding of their contents, for both customers and product manufacturers. We built a system that automatically summarizes a large collection of product reviews to generate a concise summary. Importantly, our system not only extracts the review sentiments but also the underlying justification for their opinion. We solve this problem through a novel application of clustering and validate our approach through an empirical study, obtaining good performance as judged by F-measure (the harmonic mean of purity and inverse purity).
Min-Yen Kan
Associate Professor
WING lead; interests include Digital Libraries, Information Retrieval and Natural Language Processing.