Identifying Appraisal Expressions of Online Reviews in Chinese

With the development of Web2.0 technology, an increasing number of consumers are giving comments on products over the Internet, thus opinion mining rises in response to the requirement of retrieving valuable information in speed. After thoroughly analyzing the style of language and the ways of expression in Chinese, this paper proposes a semantic lexicon-based method to identify the appraisal expressions in Chinese online reviews. A comparative experiment based on cellphone online reviews in Chinese is conducted in this research, and the result indicates that the proposed method is quite promising and outperforms the two baselines (a statistic orientation method and a semantic orientation method). Moreover, the method is applied to a comparative evaluation of two popular cellphones, demonstrating the theoretical significance and the practical value of this research.

[1]  Yubo Chen,et al.  Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix , 2004, Manag. Sci..

[2]  Chun Chen,et al.  Opinion Word Expansion and Target Extraction through Double Propagation , 2011, CL.

[3]  Raymond Y. K. Lau,et al.  Automatic Domain Ontology Extraction for Context-Sensitive Opinion Mining , 2009, ICIS.

[4]  Eric Chang,et al.  Red Opal: product-feature scoring from reviews , 2007, EC '07.

[5]  Qiang Dong,et al.  Hownet and the Computation of Meaning: (With CD-ROM) , 2006 .

[6]  Philip S. Yu,et al.  A holistic lexicon-based approach to opinion mining , 2008, WSDM '08.

[7]  Xinying Xu,et al.  Hidden sentiment association in chinese web opinion mining , 2008, WWW.

[8]  Michael Wooldridge,et al.  Proceedings of the 21st International Joint Conference on Artificial Intelligence , 2009 .

[9]  Yulan He,et al.  Joint sentiment/topic model for sentiment analysis , 2009, CIKM.

[10]  Qiang Dong,et al.  Hownet And The Computation Of Meaning , 2006 .

[11]  B. Gu,et al.  The impact of online user reviews on hotel room sales , 2009 .

[12]  Hao Yu,et al.  Extracting Product Features and Sentiments from Chinese Customer Reviews , 2010, LREC.

[13]  Quang-Thuy Ha,et al.  A Feature-Based Opinion Mining Model on Product Reviews in Vietnamese , 2011 .

[14]  Eric T. Bradlow,et al.  Automated Marketing Research Using Online Customer Reviews , 2011 .

[15]  Clement T. Yu,et al.  Topic Sentiment Change Analysis , 2011, MLDM.

[16]  Xiaoyan Zhu,et al.  Movie review mining and summarization , 2006, CIKM '06.

[17]  Pattarachai Lalitrojwong,et al.  Mining Feature-Opinion in Online Customer Reviews for Opinion Summarization , 2010, J. Univers. Comput. Sci..

[18]  Shlomo Argamon,et al.  Extracting Appraisal Expressions , 2007, NAACL.

[19]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[20]  Claire Cardie,et al.  OpinionFinder: A System for Subjectivity Analysis , 2005, HLT.

[21]  Kuiyu Chang,et al.  Mining Chinese Reviews , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[22]  Raymond Y. K. Lau,et al.  Toward a Fuzzy Domain Ontology Extraction Method for Adaptive e-Learning , 2009, IEEE Transactions on Knowledge and Data Engineering.

[23]  Wanxiang Che,et al.  Appraisal Expression Recognition with Syntactic Path for Sentence Sentiment Classification , 2011, Int. J. Comput. Process. Orient. Lang..

[24]  Raymond Y. K. Lau,et al.  Leveraging the web context for context-sensitive opinion mining , 2009, 2009 2nd IEEE International Conference on Computer Science and Information Technology.

[25]  Oren Etzioni,et al.  Extracting Product Features and Opinions from Reviews , 2005, HLT.

[26]  Raymond Y. K. Lau,et al.  Social analytics: Learning fuzzy product ontologies for aspect-oriented sentiment analysis , 2014, Decis. Support Syst..

[27]  Ivan Titov,et al.  A Joint Model of Text and Aspect Ratings for Sentiment Summarization , 2008, ACL.

[28]  Zizhuo Wang,et al.  A unified framework for dynamic pari-mutuel information market design , 2009, EC '09.

[29]  Q. Ye,et al.  The impact of e-word-of-mouth on the online popularity of restaurants: a comparison of consumer reviews and editor reviews. , 2010 .

[30]  Daniel Zeng,et al.  Fine-grained opinion mining by integrating multiple review sources , 2010 .