The Opinion Mining Based on Fuzzy Domain Sentiment Ontology Tree for Product Reviews

In this paper, we present a novel method that integrates domain sentiment knowledge into the analysis approach to deal with feature-level opinion mining By constructing a domain ontology called Fuzzy Domain Sentiment Ontology Tree (FDSOT), we then utilize the prior sentiment knowledge of our ontology to achieve significantly accuracy in sentiment classification. Particularly, the FDSOT is the conceptual model which represents the semantic relation between features and sentiment words. The evaluation is based on the Chinese product reviews collected from 360buy.com 1 . The experimental results demonstrate that our approach is able to automatically identify the domain-dependent polarity for a large subset of sentiment expression and effectively improve the performance of opinion mining.

[1]  Hua Xu,et al.  Clustering product features for opinion mining , 2011, WSDM '11.

[2]  Panagiotis G. Ipeirotis,et al.  Show me the money!: deriving the pricing power of product features by mining consumer reviews , 2007, KDD '07.

[3]  Lei Zhang,et al.  Extracting Resource Terms for Sentiment Analysis , 2011, IJCNLP.

[4]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993, Knowl. Acquis..

[5]  Ke Xu,et al.  MoodLens: an emoticon-based sentiment analysis system for chinese tweets , 2012, KDD.

[6]  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.

[7]  Peter D. Turney.  Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[8]  Gerhard Weikum,et al.  A Weakly Supervised Model for Sentence-Level Semantic Orientation Analysis with Multiple Experts , 2012, EMNLP.

[9]  Lina Zhou,et al.  Ontology-supported polarity mining , 2008 .

[10]  Ming Xu,et al.  An Improved Method to Building a Score Lexicon for Chinese Sentiment Analysis , 2012, 2012 Eighth International Conference on Semantics, Knowledge and Grids.

[11]  Kam-Fai Wong,et al.  Learning Knowledge from Relevant Webpage for Opinion Analysis , 2008, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[12]  Thomas R. Gruber,et al.  A Translation Approach to Portable Ontologies , 1993 .

[13]  Zushu Li,et al.  A Novel Chinese Domain Ontology Construction Method for Petroleum Exploration Information , 2012, J. Comput..

[14]  Janyce Wiebe,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2005, HLT.

[15]  Jon Atle Gulla,et al.  Sentiment Learning on Product Reviews via Sentiment Ontology Tree , 2010, ACL.

[16]  Chanle Wu,et al.  A New Intelligent Topic Extraction Model on Web , 2011, J. Comput..

[17]  Tony Veale,et al.  A Context-sensitive, Multi-faceted Model of Lexico-Conceptual Affect , 2012, ACL.

[18]  Lina Zhou,et al.  Ontology-supported polarity mining , 2008, J. Assoc. Inf. Sci. Technol..

[19]  Fadi Biadsy,et al.  Contextual Phrase-Level Polarity Analysis Using Lexical Affect Scoring and Syntactic N-Grams , 2009, EACL.

[20]  Shan Li,et al.  Tag Ontology Automatic Building for Semantic Searching of Services: a Case Study on Mashup Services , 2012, J. Comput..