Adaptive subjective triggers for opinionated document retrieval

This paper proposes a novel application of a statistical language model to opinionated document retrieval targeting weblogs (blogs). In particular, we explore the use of the trigger model—originally developed for incorporating distant word dependencies—in order to model the characteristics of personal opinions that cannot be properly modeled by standard n-grams. Our primary assumption is that there are two constituents to form a subjective opinion. One is the subject of the opinion or the object that the opinion is about, and the other is a subjective expression; the former is regarded as a triggering word and the latter as a triggered word. We automatically identify those subjective trigger patterns to build a language model from a corpus of product customer reviews. Experimental results on the TREC Blog Track test collections show that, when used for reranking initial search results, our proposed model significantly improves opinionated document retrieval by over 20% in MAP. In...

• Type: Conference
• Year: 2009
• Where: WSDM
• Authors: Kazuhiro Seki, Kuniaki Uehara
• Keywords: Data Mining, Document Retrieval, Language Models, Retrieval Targeting Weblogs, Wsdm 2009