Facet-based opinion retrieval from blogs
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摘要
The paper presents methods of retrieving blog posts containing opinions about an entity expressed in the query. The methods use a lexicon of subjective words and phrases compiled from manually and automatically developed resources. One of the methods uses the Kullback–Leibler divergence to weight subjective words occurring near query terms in documents, another uses proximity between the occurrences of query terms and subjective words in documents, and the third combines both factors. Methods of structuring queries into facets, facet expansion using Wikipedia, and a facet-based retrieval are also investigated in this work. The methods were evaluated using the TREC 2007 and 2008 Blog track topics, and proved to be highly effective.
论文关键词:Opinion retrieval,Information retrieval,Blogs,Sentiment analysis
论文评审过程:Received 12 January 2009, Revised 15 June 2009, Accepted 17 June 2009, Available online 17 July 2009.
论文官网地址:https://doi.org/10.1016/j.ipm.2009.06.005