Abstract
Customer reviews provide opinions and relevant information that will affect the purchase behavior of other customers. Many studies have focused on the prediction of the helpfulness rate of customer reviews to find the helpful reviews which are traditionally determined by the helpful voting results. In our study, we find that the helpfulness voting result of an online review is not constant over time. Therefore, predicting the voting result based on the analysis of text is not enough; the temporal issue must be considered. We propose a system that can rank the reviews based on a set of linguistic features with a linear regression model. To evaluate our system, we collect Chinese custom reviews in eight product categories (books, digital cameras, tablet PC, backpacks, movies, men shoes, toys, and cell phones) from Amazon.cn with the voting result on the helpfulness of the reviews. Since the voting result may be affected by voting time and total voting number, we define a new evaluation index and compare the regression results. The results show that the system has less prediction error when it takes the time information into the prediction model.
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References
J. Mackiewicz, D. Yeats, Product review users’ perceptions of review quality: the role of credibility, informativeness, and readability. IEEE Trans. Prof. Commun. 57(4), 309–324 (2014)
J. Iio, Evaluating the usefulness of online reviews, in 15th International Conference on Network-Based Information Systems (NBiS), 26–28 September 2012
R. Zhang, X. He, A. Zhou, C. Sha, Online evaluation re-scoring based on review behavior analysis, in IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 17–20 August 2014
W. Hui-Po, The evolution analysis of the opinion network of Chinese farmers’ adoption of mobile commerce, in 20th International Conference on Management Science and Engineering, 17–19 July 2013
Y. Li, W. Mao, D. Zeng, L. Huangfu, C. Liu, Extracting opinion explanations from chinese online reviews, in IEEE International Conference on Intelligence and Security Informatics (ISI), 11–14 June 2012
T.C. Peng, C.C. Shih, Using Chinese Part-of-Speech Patterns for Sentiment Phrase Identification and Opinion Extraction in User Generated Reviews. To appear in ICDIM, 2010
W. Wei, H. Liu, J. He, H. Yang, X. Du, Extracting feature and opinion words effectively from Chinese product reviews, in Fifth International Conference on Fuzzy Systems and Knowledge Discovery FSKD’08, 18–20 October 2008
Z. Zhang, J. Qi, G. Zhu, Mining customer requirement from helpful online reviews, in Second International Conference on Enterprise Systems (2014)
J.P. Lander, R for Everyone: Advanced Analytics and Graphics, 1st edn. (Addison-Wesley Professional, Boston, 2013)
GooSeeker, http://www.gooseeker.com/
CKIP, Chinese online parser service. http://parser.iis.sinica.edu.tw/
Y. Lu, P. Tsaparas, A. Ntoulas, L. Polanyi, Exploiting social context for review quality prediction, in Proceedings of the 19th International Conference on World wide Web (Raleigh, North Carolina, USA, 2010)
L.-W. Ku, T.-H. Huang, H.-H. Chen. Using morphological and syntactic structures for Chinese opinion analysis, in Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (Singapore, 2009), vol. 3
H.-Y. Hsieh, V. Klyuev, Q. Zhao, S.-H. Wu, SVR-based outlier detection and its application to hotel ranking, in IEEE 6th International Conference on Awareness Science and Technology (iCAST) (2014)
C.-R. Li, Y. Chi-Hsin, H.-H. Chen, Predicting the semantic orientation of terms in E-HowNet. Comput. Linguistics Chinese Lang. Process. 17(2), 21–36 (2012). (in Chinese)
Y.-C. Zeng, S.-H. Wu, Modeling the helpful opinion mining of online consumer reviews as a classification problem, in IJCNLP Workshop on Natural Language Processing for Social Media (SocialNLP) (2013), pp. 29–35
H.-X. Shi, X.-J. Li, A sentiment analysis model for hotel reviews based on supervised learning, in Proceedings of the International Conference on Machine Learning and Cybernetics (Guilin, 2011)
S. Tan, J. Zhang, An empirical study of sentiment analysis for Chinese documents. Expert Syst. Appl. 34(4), 2622–2629 (2008)
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Wu, SH., Hsieh, YH., Chen, LP., Yang, PC., Fanghuizhu, L. (2019). Temporal Model of the Online Customer Review Helpfulness Prediction with Regression Methods. In: Kaya, M., Alhajj, R. (eds) Influence and Behavior Analysis in Social Networks and Social Media. ASONAM 2018. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-02592-2_2
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DOI: https://doi.org/10.1007/978-3-030-02592-2_2
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