A new generative opinion retrieval model integrating multiple ranking factors

Authors:
Seung-Wook Lee;Young-In Song;Jung-Tae Lee;Kyoung-Soo Han;Hae-Chang Rim
Affiliations:
Department of Computer and Radio Communications Engineering, Korea University, Seoul, South Korea;Microsoft Research Asia, Beijing, People's Republic of China;Department of Computer and Radio Communications Engineering, Korea University, Seoul, South Korea;Division of Computer Engineering, Sungkyul University, Kyunggi, South Korea;Department of Computer and Radio Communications Engineering, Korea University, Seoul, South Korea
Venue:
Journal of Intelligent Information Systems
Year:
2012

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Abstract

In this paper, we present clear and formal definitions of ranking factors that should be concerned in opinion retrieval and propose a new opinion retrieval model which simultaneously combines the factors from the generative modeling perspective. The proposed model formally unifies relevance-based ranking with subjectivity detection at the document level by taking multiple ranking factors into consideration: topical relevance, subjectivity strength, and opinion-topic relatedness. The topical relevance measures how strongly a document relates to a given topic, and the subjectivity strength indicates the likelihood that the document contains subjective information. The opinion-topic relatedness reflects whether the subjective information is expressed with respect to the topic of interest. We also present the universality of our model by introducing the model's derivations that represent other existing opinion retrieval approaches. Experimental results on a large-scale blog retrieval test collection demonstrate that not only are the individual ranking factors necessary in opinion retrieval but they cooperate advantageously to produce a better document ranking when used together. The retrieval performance of the proposed model is comparable to that of previous systems in the literature.