Summary of the paper
| Title | Extracting Product Features and Sentiments from Chinese Customer Reviews |
|---|---|
| Authors | Shu Zhang, Wenjie Jia, Yingju Xia, Yao Meng and Hao Yu |
| Abstract | With the growing interest in opinion mining from web data, more works are focused on mining in English and Chinese reviews. Probing into the problem of product opinion mining, this paper describes the details of our language resources, and imports them into the task of extracting product feature and sentiment task. Different from the traditional unsupervised methods, a supervised method is utilized to identify product features, combining the domain knowledge and lexical information. Nearest vicinity match and syntactic tree based methods are proposed to identify the opinions regarding the product features. Multi-level analysis module is proposed to determine the sentiment orientation of the opinions. With the experiments on the electronic reviews of COAE 2008, the validities of the product features identified by CRFs and the two opinion words identified methods are testified and compared. The results show the resource is well utilized in this task and our proposed method is valid. |
| Topics | Text mining, Information Extraction, Information Retrieval, Semantics |
Full paper |
Extracting Product Features and Sentiments from Chinese Customer Reviews |
Slides |
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| Bibtex | @InProceedings{ZHANG10.583, author = {Shu Zhang and Wenjie Jia and Yingju Xia and Yao Meng and Hao Yu}, title = {Extracting Product Features and Sentiments from Chinese Customer Reviews}, booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |