A new generative opinion retrieval model integrating multiple ranking factors
Journal of Intelligent Information Systems
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In this paper, we discuss the the role of the retrieval component in an TREC style opinion question answering system. Since blog retrieval differs from traditional ad-hoc document retrieval, we need to work on dedicated retrieval methods. In particular we focus on a new query expansion technique to retrieve people’s opinions from blog posts. We propose a combined approach for expanding queries while considering two aspects: finding more relevant data, and finding more opinionative data. We introduce a method to select opinion bearing terms for query expansion based on a chi-squared test and use this new query expansion to combine it in a liner weighting scheme with the original query terms and relevant feedback terms from web. We report our experiments on the TREC 2006 and TREC 2007 queries from the blog retrieval track. The results show that the methods investigated here enhanced mean average precision of document retrieval from 17.91% to 25.20% on TREC 2006 and from 22.28% to 32.61% on TREC 2007 queries.