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.
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Notes
They are based on their own baseline topical retrieval techniques instead of the five baseline results that TREC offered.
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This work was supported by the Second Brain Korea 21 Project.
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Lee, SW., Song, YI., Lee, JT. et al. A new generative opinion retrieval model integrating multiple ranking factors. J Intell Inf Syst 38, 487–505 (2012). https://doi.org/10.1007/s10844-011-0164-5
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DOI: https://doi.org/10.1007/s10844-011-0164-5