We use cookies to improve your experience with our site.

Indexed in:

SCIE, EI, Scopus, INSPEC, DBLP, CSCD, etc.

Submission System
(Author / Reviewer / Editor)
Advanced Search
Volume 30 Issue 5
September  2015
Turn off MathJax
Article Contents
Abstract
References
Related Articles
Yu Zhang, Miao Liu, Hai-Xia Xia. Clustering Context-Dependent Opinion Target Words in Chinese Product Reviews[J]. Journal of Computer Science and Technology, 2015, 30(5): 1109-1119. DOI: 10.1007/s11390-015-1586-2
Citation: Yu Zhang, Miao Liu, Hai-Xia Xia. Clustering Context-Dependent Opinion Target Words in Chinese Product Reviews[J]. Journal of Computer Science and Technology, 2015, 30(5): 1109-1119. DOI: 10.1007/s11390-015-1586-2
shu

Clustering Context-Dependent Opinion Target Words in Chinese Product Reviews

  • School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China

Funds: This work was supported by the Commonweal Technical Project of Zhejiang Province of China under Grant No. 2013C33063, the National Natural Science Foundation of China under Grant Nos. 61100183, 61402417, the Natural Science Foundation of Zhejiang Province of China under Grant No. LQ13F020014, and the 521 Talents Project of Zhejiang Sci-Tech University.
More Information
  • Author Bio:

    Yu Zhang now is an associate professor of Zhejiang Sci-Tech University, Hangzhou. She received her Ph.D. degree in computer science and technology from Zhejiang University, Hangzhou, in 2009. She is a member of CCF. Her current research interests include data mining, recommender system, and sentiment analysis.

  • Corresponding author:

    Yu Zhang E-mail: yzh@zstu.edu.cn

  • Received Date: November 14, 2014
  • Revised Date: June 07, 2015
  • Published Date: September 04, 2015
    Abstract
  • In opinion mining of product reviews, an important task is to provide a summary of customers' opinions based on different opinion targets. Due to various knowledge backgrounds or linguistic habits, customers use a variety of terms to describe the same opinion target. These terms are called as context-dependent synonyms. In order to provide a comprehensive summary, the first step is to classify these opinion target words into groups. In this article, we mainly focus on clustering context-dependent opinion target words in Chinese product reviews. We utilize three clustering methods based on distributional similarity and use four different co-occurrence matrices for experiments. According to the experimental results on a large number of reviews, we find that our proposed heuristic k-means clustering method using opinion target words co-occurrence matrix achieves the best clustering result with lower time complexity and less memory space. In addition, the accuracy is more stable when choosing different combinations of centroids. For some kinds of co-occurrence matrices, we also find that using small-size (low-dimensional) matrices achieves higher average clustering accuracy than using large-size (high-dimensional) matrices. Our findings provide a time-efficient and space-efficient way to cluster opinion targets with high accuracy.
    • FullText(HTML)
    • References (24)
  • [1]
    Li D, Shuai X, Sun G, Tang J, Ding Y, Luo Z. Mining topiclevel opinion influence in microblog. In Proc. the 21st ACM International Conference on Information and Knowledge Management, Oct. 29-Nov. 2, 2012, pp.1562-1566.
    [2]
    Socher R, Perelygin A, Wu J Y, Chuang J, Manning C D, Ng A Y, Potts C. Recursive deep models for semantic compositionality over a sentiment treebank. In Proc. the 2013 Conference on Empirical Methods in Natural Language Processing, Oct. 2013, pp.1631-1642.
    [3]
    Poria S, Cambria E, Winterstein G, Huang G B. Sentic patterns:Dependency-based rules for concept-level sentiment analysis. Knowledge-Based Systems, 2014, 69:45-63.
    [4]
    Zhai Z, Liu B, Xu H, Jia P. Constrained LDA for grouping product features in opinion mining. In Proc. the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Part 1, May 2011, pp.448-459.
    [5]
    Cambria E, Mazzocco T, Hussain A, Eckl C. Sentic medoids:Organizing affective common sense knowledge in a multi-dimensional vector space. In Proc. the 8th International Symposium on Neural Networks, Part 3, May 29-Jun. 1, 2011, pp.601-610.
    [6]
    Cambria E, Hussain A, Havasi C, Eckl C, Munro J. Towards crowd validation of the UK national health service. In Proc. the Web Science Conference 2010, Apr. 2010.
    [7]
    Deshpande B. How to use clustering for product categorization or segmentation. Feb. 2013. http://www.simafore.com-/blog/bid/113689/How-to-use-clustering-for-product-categorization-or-segmentation, Aug. 2015.
    [8]
    Agirre E, Alfonseca E, Hall K, Kravalova J, Pasca M, Soroa A. A study on similarity and relatedness using distributional and WordNet-based approaches. In Proc. the 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies, May 31-Jun. 5, 2009, pp.19-27.
    [9]
    Carenini G, Ng R T, Zwart E. Extracting knowledge from evaluative text. In Proc. the 3rd International Conference on Knowledge Capture, Oct. 2005, pp.11-18.
    [10]
    Wagstaff K, Cardie C, Rogers S, Schrödl S. Constrained kmeans clustering with background knowledge. In Proc. the 18th International Conference on Machine Learning, Jun. 28-Jul. 1, 2001, pp.577-584.
    [11]
    Zhai Z, Liu B, Xu H, Jia P. Clustering product features for opinion mining. In Proc. the 4th International Conference on Web Search and Data Mining, Feb. 2011, pp.347-354.
    [12]
    Lin D, Wu X. Phrase clustering for discriminative learning. In Proc. the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the AFNLP, Aug. 2009, pp.1030-1038.
    [13]
    Deerwester S, Dumais S T, Furnas G W, Landauer T K, Harshman R. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 1990, 41(6):391-407.
    [14]
    Sahami M, Heilman T D. A web-based kernel function for measuring the similarity of short text snippets. In Proc. the 15th International Conference on World Wide Web, May 2006, pp.377-386.
    [15]
    Bu F, Zhu X, Li M. Measuring the non-compositionality of multiword expressions. In Proc. the 23rd International Conference on Computational Linguistics, Aug. 2010, pp.116-124.
    [16]
    Pantel P, Crestan E, Borkovsky A, Popescu A M, Vyas V. Web-scale distributional similarity and entity set expansion. In Proc. the 2009 Conference on Empirical Methods in Natural Language Processing, Aug. 2009, pp.938-947.
    [17]
    Andrzejewski D, Zhu X, Craven M. Incorporating domain knowledge into topic modeling via Dirichlet Forest priors. In Proc. the 26th Annual International Conference on Machine Learning, Jun. 2009, pp.25-32.
    [18]
    Zhao S, Liu T, Li S. A topical document clustering method. Journal of Chinese Information Processing, 2007, 21(2):58-62. (in Chinese)
    [19]
    Elsner M, Charniak E, Johnson M. Structured generative models for unsupervised named-entity clustering. In Proc. the 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies, May 31-Jun. 5, 2009, pp.164- 172.
    [20]
    Andrews N, Eisner J, Dredze M. Robust entity clustering via phylogenetic inference. In Proc. the 52nd Annual Meeting of the Association for Computational Linguistics, Vol. 1:Long Papers, Jun. 2014, pp.775-785.
    [21]
    Green S, Andrewst N, Gormleyt M R, Dredzet M, Manning C D. Entity clustering across languages. In Proc. the 2012 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies, Jun. 2012, pp.60-69.
    [22]
    Chen J, Zhao Z, Ye J, Liu H. Nonlinear adaptive distance metric learning for clustering. In Proc. the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2007, pp.123-132.
    [23]
    Li F, Han C, Huang M, Zhu X, Xia Y, Zhang S, Yu H. Structure-aware review mining and summarization. In Proc. the 23rd International Conference on Computational Linguistics, Aug. 2010, pp.653-661.
    [24]
    Zhang Y, Zhu W. Extracting implicit features in online customer reviews. In Proc. the 22nd International Conference on World Wide Web Companion, May 2013, pp.103-104.
    • Relative Articles

  • Related Articles

    [1]Fu-Zhen Zhuang, Ying-Min Zhou, Hao-Chao Ying, Fu-Zheng Zhang, Xiang Ao, Xing Xie, Qing He, Hui Xiong. Sequential Recommendation via Cross-Domain Novelty Seeking Trait Mining[J]. Journal of Computer Science and Technology, 2020, 35(2): 305-319. DOI: 10.1007/s11390-020-9945-z
    [2]Jun-Wen Duan, Yi-Heng Chen, Ting Liu, Xiao Ding. Mining Intention-Related Products on Online Q&A Community[J]. Journal of Computer Science and Technology, 2015, 30(5): 1054-1062. DOI: 10.1007/s11390-015-1581-7
    [3]Lei Fang, Biao Liu, Min-Lie Huang. Leveraging Large Data with Weak Supervision for Joint Feature and Opinion Word Extraction[J]. Journal of Computer Science and Technology, 2015, 30(4): 903-916. DOI: 10.1007/s11390-015-1569-3
    [4]Su-Ke Li, Zhi Guan, Li-Yong Tang, Zhong Chen. Exploiting Consumer Reviews for Product Feature Ranking[J]. Journal of Computer Science and Technology, 2012, 27(3): 635-649. DOI: 10.1007/s11390-012-1250-z
    [5]Bo Yang, Da-You Liu. A Heuristic Clustering Algorithm for Mining Communities in Signed Networks[J]. Journal of Computer Science and Technology, 2007, 22(2): 320-328.
    [6]HUANG Liusheng, CHEN Huaping, WANG Xun, CHEN Guoliang. A Fast Algorithm for Mining Association Rules[J]. Journal of Computer Science and Technology, 2000, 15(6): 619-624.
    [7]TAO Xiaopeng, ZHOU Aoying, HU Yunfa. Fast Algorithms of Mining Probability Functional Dependency Rules in Relational Database[J]. Journal of Computer Science and Technology, 2000, 15(3): 261-270.
    [8]ZHOU Aoying, JIN Wen, ZHOU Shuigeng, QIAN Weining, TIAN Zenping. Incremental Mining of the Schema of Semistructured Data[J]. Journal of Computer Science and Technology, 2000, 15(3): 241-248.
    [9]Fan Jianhua, Li Deyi. An Overview of Data Mining and Knowledge Discovery[J]. Journal of Computer Science and Technology, 1998, 13(4): 348-368.
    [10]Jiao Guofang, Liu Shenquan. Information Model for Product Modeling[J]. Journal of Computer Science and Technology, 1992, 7(1): 47-55.
    • Supplements (0)

Catalog

    Get Citation
    PDF
    XML
    Article views (36) PDF downloads (947) Cited by()
    Related

    Copyright ©2025 JCST, All Rights Reserved     京ICP备05002829号-1

    Institute of Computing Technology, Chinese Academy of Sciences
    P.O. Box 2704, Beijing 100190, P.R. China

    Tel.: 86-10-62610746 E-mail: jcst@ict.ac.cn

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return