Product review summarization from a deeper perspective

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). Categories and Subject Descriptors ontent Analysis and Indexing]: Abstracting methods; I.2.7 [Natural Language Processing]: Text analysis General Terms Algorithms, Experimentation, Languages, Performance Keywords Sentiment Analysis, Summarization, Clustering

• Type: Journal
• Year: 2011
• Where: JCDL
• Authors: Duy Khang Ly, Kazunari Sugiyama, Ziheng Lin, Min-Yen Kan
• Keywords: Education, Harmonic Mean, JCDL 2011, Natural Language Processing, Sentiment Analysis