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Feature-based opinion extraction is a task related to infor- mation extraction, which consists of extracting structured opinions on features of some object from reviews or other subjective textual sources. Over the last years, this prob-lem has been studied by some researchers, generally in an unsupervised, domain-independent manner. As opposed to that, in this work we propose a rede nition of the problem from a more practical point of view, and describe a domain- speci c, resource-based opinion extraction system. We fo-cus on the description and generation of those resources, and brie y report the extraction system architecture and a few initial experiments. The results suggest that domain-speci c knowledge is a valuable resource in order to build precise opinion extraction systems.

In this paper, we provide an overview of the 3rd International Workshop on Search and Mining User-generated Contents, held in conjunction with the 20th ACM International Conference on Information and Knowledge Management. We present the motivation and goals of the workshop, and some statistics and details about accepted papers and keynotes.