Ranking blog posts that express opinions regarding a given topic should serve a critical function in helping users. We explored three types of opinion retrieval methods in the framework of probabilistic language models. The first method combines topic-relevance model and opinion-relevance model that captures topic dependence of the opinion expressions. The second method makes use of probability that any of opinion-bearing words appear in each target document as document prior probability in query-likelihood model. The third method makes use of probability that any of adjectives or adverbs appear in each target document as document prior probability, assuming opinionated documents tend to contain more adjectives or adverbs than other documents.
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