Ranking opinionated blog posts using OpinionFinder

He, B., Macdonald, C. and Ounis, I. (2008) Ranking opinionated blog posts using OpinionFinder. In: 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Singapore, 20-24 July 2008, pp. 727-728. (doi: 10.1145/1390334.1390473)

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Publisher's URL: http://dx.doi.org/10.1145/1390334.1390473


The aim of an opinion finding system is not just to retrieve relevant documents, but to also retrieve documents that express an opinion towards the query target entity. In this work, we propose a way to use and integrate an opinion-identification toolkit, OpinionFinder, into the retrieval process of an Information Retrieval (IR) system, such that opinionated, relevant documents are retrieved in response to a query. In our experiments, we vary the number of top-ranked documents that must be parsed in response to a query, and investigate the effect on opinion retrieval performance and required parsing time. We find that opinion finding retrieval performance is improved by integrating OpinionFinder into the retrieval system, and that retrieval performance grows as more posts are parsed by OpinionFinder. However, the benefit eventually tails off at a deep rank, suggesting that an optimal setting for the system has been achieved.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:He, Mr Ben and Macdonald, Professor Craig and Ounis, Professor Iadh
Authors: He, B., Macdonald, C., and Ounis, I.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science

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