tailieunhanh - Báo cáo khoa học: "Mining User Reviews: from Specification to Summarization Xinfan Meng Key Laboratory of Computational Linguistics "

This paper proposes a method to extract product features from user reviews and generate a review summary. This method only relies on product specifications, which usually are easy to obtain. Other resources like segmenter, POS tagger or parser are not required. At feature extraction stage, multiple specifications are clustered to extend the vocabulary of product features. Hierarchy structure information and unit of measurement information are mined from the specification to improve the accuracy of feature extraction. . | Mining User Reviews from Specification to Summarization Xinfan Meng Key Laboratory of Computational Linguistics Peking University Ministry of Education China mxf@ Houfeng Wang Key Laboratory of Computational Linguistics Peking University Ministry of Education China wanghf@ Abstract This paper proposes a method to extract product features from user reviews and generate a review summary. This method only relies on product specifications which usually are easy to obtain. Other resources like segmenter POS tagger or parser are not required. At feature extraction stage multiple specifications are clustered to extend the vocabulary of product features. Hierarchy structure information and unit of measurement information are mined from the specification to improve the accuracy of feature extraction. At summary generation stage hierarchy information in specifications is used to provide a natural conceptual view of product features. 1 Introduction Review mining and summarization aims to extract users opinions towards specific products from reviews and provide an easy-to-understand summary of those opinions for potential buyers or manufacture companies. The task of mining reviews usually comprises two subtasks product features extraction and summary generation. Hu and Liu 2004a use association mining methods to find frequent product features and use opinion words to predict infrequent product features. . Popescu and O. Etzioni 2005 proposes OPINE an unsupervised information extraction system which is built on top of the Kon-wItAll Web information-extraction system. In order to reduce the features redundancy and provide a conceptual view of extracted features G. Carenini et al. 2006a enhances the earlier work of Hu and Liu 2004a by mapping the extracted features into a hierarchy of features which describes the entity of interest. M. Gamon et al. 2005 clusters sentences in reviews then label each cluster with a keyword and finally provide a tree map .