Integrating proximity to subjective sentences for blog opinion retrieval

Santos, R.L.T., He, B., Macdonald, C. and Ounis, I. (2009) Integrating proximity to subjective sentences for blog opinion retrieval. In: Proceedings of the 31st European Conference on Information Retrieval (ECIR 2009), Toulouse, France, 6-9 April, 2009, pp. 325-336. (doi:10.1007/978-3-642-00958-7_30)

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Publisher's URL: http://www.springerlink.com/content/gg8873358l662973/

Abstract

Opinion finding is a challenging retrieval task, where it has been shown that it is especially difficult to improve over a strongly performing topic-relevance baseline. In this paper, we propose a novel approach for opinion finding, which takes into account the proximity of query terms to subjective sentences in a document. We adapt two state-of-the-art opinion detection techniques to identify subjective sentences from the retrieved documents. Our first technique uses the OpinionFinder toolkit to classify the subjectiveness of sentences in a document. Our second technique uses an automatically generated dictionary of subjective terms derived from the document collection itself to identify the most subjective sentences in a document. We extend the Divergence From Randomness (DFR) proximity model to integrate the proximity of query terms to the subjective sentences identified by either of the proposed techniques. We evaluate these techniques on five different strong baselines across two different query datasets from the TREC Blog track. We show that we can significantly improve over the baselines and that, in several settings, our proposed techniques can at least match the top performing systems at the TREC Blog track.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:He, Mr Ben and Macdonald, Dr Craig and Ounis, Professor Iadh
Authors: Santos, R.L.T., He, B., Macdonald, C., and Ounis, I.
Subjects:Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Research Group:Information Retrieval

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