Discovering Chinese sentence patterns for feature-based opinion summarization

This study ascertains part-of-speech patterns in Chinese opinion sentences from customer reviews.A feature-based summarization algorithm is proposed to calculate feature scores based on sentence patterns in reviews.The extraction of feature words and opinion/feeling words can be performed in the same phase to improve efficiency.The patterns can be used in different domains.The proposed algorithm works well with Chinese reviews and outperforms the baseline. This study discovers part-of-speech (POS) patterns of sentences that express opinions in Chinese product reviews. The use of these patterns makes it possible to identify opinion sentences, feature words, and opinion/feeling words. Degree words and negation words are used in determining the orientation of opinions as well as the degree of their intensity. In order to identify the subject of opinions, the associations between opinion/feeling words, feature words, and corresponding features were ascertained. An algorithm for feature-based opinion summarization is then proposed based on these patterns and association rules. Both car and movie reviews were collected for discovering patterns and testing of the patterns and algorithm. The experimental results demonstrate that the proposed algorithm and approaches perform well on Chinese product reviews.

[1]  Jingbo Zhu,et al.  Aspect-Based Opinion Polling from Customer Reviews , 2011, IEEE Transactions on Affective Computing.

[2]  David Schuff,et al.  What Makes a Helpful Review? A Study of Customer Reviews on Amazon.com , 2010 .

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

[4]  Xiaohui Yu,et al.  Modeling and Predicting the Helpfulness of Online Reviews , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[5]  Bing Liu,et al.  Opinion observer: analyzing and comparing opinions on the Web , 2005, WWW '05.

[6]  Chih-Ping Wei,et al.  Understanding what concerns consumers: a semantic approach to product feature extraction from consumer reviews , 2010, Inf. Syst. E Bus. Manag..

[7]  Xiangji Huang,et al.  Mining Online Reviews for Predicting Sales Performance: A Case Study in the Movie Domain , 2012, IEEE Transactions on Knowledge and Data Engineering.

[8]  Japinder Singh,et al.  Feature-based opinion mining and ranking , 2012, J. Comput. Syst. Sci..

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

[10]  Hongyan Liu,et al.  Combining user preferences and user opinions for accurate recommendation , 2013, Electron. Commer. Res. Appl..

[11]  Michael V. Mannino,et al.  Linguistic characteristics of shill reviews , 2014, Electron. Commer. Res. Appl..

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

[13]  R. Dhar,et al.  Consumer Choice between Hedonic and Utilitarian Goods , 2000 .

[14]  Zhou Wei,et al.  Sentiment analysis of Chinese micro-blog using semantic sentiment space model , 2012, Proceedings of 2012 2nd International Conference on Computer Science and Network Technology.

[15]  Bing Liu,et al.  Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data , 2006, Data-Centric Systems and Applications.

[16]  Chia Chun Shih,et al.  Using Chinese part-of-speech patterns for sentiment phrase identification and opinion extraction in user generated reviews , 2010, 2010 Fifth International Conference on Digital Information Management (ICDIM).

[17]  Hsin-Hsi Chen,et al.  Mining opinions from the Web: Beyond relevance retrieval , 2007 .

[18]  Chien-Liang Liu,et al.  Movie Rating and Review Summarization in Mobile Environment , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[19]  Meng Wang,et al.  Domain-Assisted Product Aspect Hierarchy Generation: Towards Hierarchical Organization of Unstructured Consumer Reviews , 2011, EMNLP.

[20]  Bing Xu,et al.  An unsupervised approach to rank product reviews , 2011, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[21]  Andrés Montoyo,et al.  Detecting Implicit Expressions of Sentiment in Text Based on Commonsense Knowledge , 2011, WASSA@ACL.

[22]  Xueqi Cheng,et al.  Aspect-based extractive summarization of online reviews , 2011, SAC '11.

[23]  Kam-Fai Wong,et al.  An efficient approach for sentence-based opinion retrieval , 2010, ICMLC.

[24]  Hongyan Liu,et al.  CRO: a system for online review structurization , 2008, KDD.

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

[26]  Richong Zhang,et al.  An information gain-based approach for recommending useful product reviews , 2011, Knowledge and Information Systems.

[27]  Wen Shi,et al.  Sentiment Classification for Movie Reviews in Chinese by Improved Semantic Oriented Approach , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[28]  Xiaoping Yang,et al.  Research on Sentiment Tendency Analysis of Microtext Based on Sense Group , 2013, 2013 Ninth International Conference on Computational Intelligence and Security.

[29]  Zhong Su,et al.  Product feature categorization with multilevel latent semantic association , 2009, CIKM.

[30]  Xin Wang,et al.  Chinese subjectivity detection using a sentiment density-based naive Bayesian classifier , 2010, 2010 International Conference on Machine Learning and Cybernetics.

[31]  Xu Zhou,et al.  Semantic inclination mining based on dependency grammar for Chinese BLOG , 2010, 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA).

[32]  Christopher S. G. Khoo,et al.  Aspect-based sentiment analysis of movie reviews on discussion boards , 2010, J. Inf. Sci..

[33]  Wei Zhang,et al.  A Dependency Tree Based Approach for Sentence-Level Sentiment Classification , 2011, 2011 12th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing.

[34]  Minlie Huang,et al.  Fine Granular Aspect Analysis using Latent Structural Models , 2012, ACL.