Fusion Approach to Finding Opinions in Blogosphere
Kiduk Yang, Ning Yu, Alejandro Valerio, Hui Zhang and Weimao Ke
In this paper, we describe a fusion approach to finding opinion about a given target in blog postings. We tackled the opinion blog retrieval task by breaking it down to two sequential subtasks: ontopic retrieval followed by opinion classification. Our opinion retrieval approach was to first apply traditional IR methods to retrieve on-topic blogs, and then boost the ranks of opinionated blogs using combined opinion scores generated by four opinion assessment methods. Our opinion module consists of Opinion Term Module, which identify opinions based on the frequency of opinion terms (i.e., terms that only occur frequently in opinion blogs), Rare Term Module, which uses uncommon/rare terms (e.g., "sooo good") for opinion classification, IU Module, which uses IU (I and you) collocations, and Adjective-Verb Module, which uses computational linguistics’ distribution similarity approach to learn the subjective language from training data.
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