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Research paper | Open Access

Research on comment target extracting in Chinese online shopping platform

Zhishuo Liu( ), Qianhui Shen, Jingmiao Ma, Ziqi Dong
School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China
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Abstract

Purpose

This paper aims to extract the comment targets in Chinese online shopping platform.

Design/methodology/approach

The authors first collect the comment texts, word segmentation, part-of-speech (POS) tagging and extracted feature words twice. Then they cluster the evaluation sentence and find the association rules between the evaluation words and the evaluation object. At the same time, they establish the association rule table. Finally, the authors can mine the evaluation object of comment sentence according to the evaluation word and the association rule table. At last, they obtain comment data from Taobao and demonstrate that the method proposed in this paper is effective by experiment.

Findings

The extracting comment target method the authors proposed in this paper is effective.

Research limitations/implications

First, the study object of extracting implicit features is review clauses, and not considering the context information, which may affect the accuracy of the feature excavation to a certain degree. Second, when extracting feature words, the low-frequency feature words are not considered, but some low-frequency feature words also contain effective information.

Practical implications

Because of the mass online reviews data, reading every comment one by one is impossible. Therefore, it is important that research on handling product comments and present useful or interest comments for clients.

Originality/value

The extracting comment target method the authors proposed in this paper is effective.

Keywords

Opinion miningAssociation ruleImplicit featuresSA-FCMSA-PSO

References

 
Agrawal, R. and Srikant, R. (1994), “Fast algorithms for mining association rules”, Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487-499.
 
Agrawal, R., Imielinski, T. and Swami, A. (1993), “mining association rules between sets of item in large database”, Proceedings of The 1993 ACM SIGMOD International Conference on Management of Data - SIGMOD’93, p. 207.https://doi.org/10.1145/170035.170072
Crossref
 
Bezdek, J.C. (1981), “Pattern recognition with fuzzy objective function algorithms”, Plenum, Vol. 22, pp. 203-239.https://doi.org/10.1007/978-1-4757-0450-1_6
Crossref
 
Brody, S. and Elhadad, N. (2010), “An unsupervised aspect-sentiment model for online reviews”, Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Association for Computational Linguistics, Los Angeles, Cafifornia, pp. 804-812.
 
Hai, Z., Chang, K. and Kim, J.J. (2011), “Implicit features identification via co-occurrence association rule mining”, International Conference on Intelligent Text Processing and Computational Linguistics and Intelligent Text Processing, pp. 393-404.https://doi.org/10.1007/978-3-642-19400-9_31
Crossref
 

Herr, P., Kardes, F.R. and Kim, J. (1991), “Effects of word-of-mouth and product-attribute information on persuation: an accessibility- diagnosticity perspective”, Journal of Consumer Research, Vol. 17 No. 4, pp. 454-462.
Crossref Google Scholar
 
Jakob, N. and Gurevych, I. (2010), “Extracting opinion targets in a single-and cross-domain setting with conditional random fields”, Proceeding of the Conference on Empirical in Natural Language Processing, pp. 1035-1045.
 

Jiang, W., Pan, H. and Ye, Q. (2014), “An improved association rule mining approach to identification of implicit product aspects”, The Open Cybernetics and Systemics Journal, Vol. 8 No. 1, pp. 924-930.
Crossref Google Scholar
 

Liao, X., Chen, X., Wei, J., et al., (2017), “A multi-layer relation graph model for extracting opinion targets and opinion words”, Acta Automatica Sinica, Vol. 43 No. 3.
Google Scholar
 

Liu, K., Xu, L. and Zhao, J. (2015), “Co-extracting opinion targets and opinion words from online reviews based on the word alignment mode”, IEEE Transactions on Knowledge and Data Engineering, Vol. 27 No. 3, pp. 636-650.
Crossref Google Scholar
 

Liu, Q., Huang, H. and Feng, C. (2016), “Co-extracting opinion targets and opinion-bearing words in chinese Micro-Blog texts”, ACTA Electronica SINICA, Vol. 44 No. 7.
Google Scholar
 

Liu, H., Zhao, Y., Qian, B. and Liu, T. (2010), “Comment target extraction and sentiment classification”, Journal of Chinese Information Processing, Vol. 24 No. 1.
Google Scholar
 

Luo, X. (2009), “Qualifying the long-term impact of negative word of mouth on cash flows and stock prices”, Marketing Science, Vol. 28 No. 1, pp. 148-165.
Crossref Google Scholar
 
Pang, B., Lee, L. and Vaithyanathan, S. (2002), “Thumbs up? Sentiment classification using machine learning techniques”, Proceeding of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79-86.https://doi.org/10.3115/1118693.1118704
Crossref
 
Pang, J.H., Li, X., Xie, H. and Rao, Y. (2016), “SBTM: topic modeling over short texts”, DASFAA 2016 Workshop, pp. 43-56.https://doi.org/10.1007/978-3-319-32055-7_4
Crossref
 
Popescu, A.M. and Etzioni, O. (2005), “OPINE: extracting product and opinion from reviews”, Proceeding of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 32-33.https://doi.org/10.3115/1225733.1225750
Crossref
 
Scaffidi, C., Bierhoff, K., Chang, E., Felker, M., Ng, H. and Jin, C. (2007), “Red opal: product-feature scoring from reviews”, Proceeding of the 8th ACM Conference on Electronic Commerce, pp. 182-191.https://doi.org/10.1145/1250910.1250938
Crossref
 
Schouten, K. and Frasincar, F. (2014), “Finding implicit features in consumer reviews for sentiment analysis”, International Conference on Web EngineerinG (ICWE2015), pp. 130-144.https://doi.org/10.1007/978-3-319-08245-5_8
Crossref
 

Wang, W., Xu, H. and Wan, W. (2013), “Implicit feature identification via hybrid association rule mining”, Expert Systems with Applications, Vol. 40 No. 9, pp. 3518-3531.
Crossref Google Scholar
 
Wu, X. and Kumar, V. (2013), The Top Ten Algorithms in Data Mining, Tsinghua University Press.
 

Xu, H., Zhang, F. and Wang, W. (2015), “Implicit feature identification in chinese reviews using explicit topic mining model”, Knowledge-Based Systems, Vol. 76, pp. 166-175.
Crossref Google Scholar
 
Yi, J., Nasukawa, T., Bunescu, R. and Niblack, W. (2003), “Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques”, in Proceeding of the Third IEEE International Conference on DataMing (ICDM’03), pp. 427-434.
 
Zhang, L., Liu, B., Lim, S. and O’Brien-Strain, E. (2010), “Extracting and ranking product features in opinion documents”, Proceeding of the 23rd International Conference on Computational Linguistics: Posters, ACL, pp. 1462-1470.
 
Zhang, Y. and Zhu, W. (2013), “Extracting implicit features in online customer reviews for opinion mining”, inProceedings of the 22td International Conference on World Wide Web Companion, pp. 103-104.https://doi.org/10.1145/2487788.2487835
Crossref
International Journal of Crowd Science
Volume 2 Issue 3,
December 2018
Pages 247-258
DOI: 10.1108/IJCS-09-2018-0019
Cite this article:
Liu Z, Shen Q, Ma J, et al. Research on comment target extracting in Chinese online shopping platform. International Journal of Crowd Science, 2018, 2(3): 247-258. https://doi.org/10.1108/IJCS-09-2018-0019

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Received: 08 September 2018
Revised: 15 October 2018
Accepted: 16 October 2018
Published: 26 November 2018
© The author(s)

Zhishuo Liu, Qianhui Shen, Jingmiao Ma and Ziqi Dong. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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