Abstract
This paper is concerned with the problem of mining commodity information from threaded Chinese customer reviews. Chinese online commodity forums, which are developing rapidly, provide a good environment for customers to share reviews. However, due to noises and navigational limitations, it is hard to have a clear view of a commodity from thousands of related reviews. Further more, due to different characters between Chinese and English, Researching approaches may vary a lot. This paper aims to automatically mine out key information from commodity reviews. An effective algorithm, i.e. Chinese Commodity Review Miner (CCRM) is proposed. The algorithm can be divided into two parts. First, we propose an efficient rule based algorithm for commodity feature extraction as well as a probabilistic model for feature ranking. Second, we propose a top-to-down algorithm to reorganize the extracted features into hierarchical structure. A prototype system based on CCRM is also implemented. Using CCRM, users can easily acquire the outline of a commodity, and navigate freely in it.
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Duan, H., Bao, S., Yu, Y. (2007). CCRM: An Effective Algorithm for Mining Commodity Information from Threaded Chinese Customer Reviews. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_48
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DOI: https://doi.org/10.1007/978-3-540-71701-0_48
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