Browse > Article
http://dx.doi.org/10.13088/jiis.2022.28.2.171

A multi-channel CNN based online review helpfulness prediction model  

Li, Xinzhe (Department of Big Data Analytics, Kyung Hee University)
Yun, Hyorim (College of Hotel and Tourism School of Hospitality Management, Kyung Hee University)
Li, Qinglong (Department of Big Data Analytics, Kyung Hee University)
Kim, Jaekyeong (School Management & Department of Big Data Analytics, Kyung Hee University)
Publication Information
Journal of Intelligence and Information Systems / v.28, no.2, 2022 , pp. 171-189 More about this Journal
Abstract
Online reviews play an essential role in the consumer's purchasing decision-making process, and thus, providing helpful and reliable reviews is essential to consumers. Previous online review helpfulness prediction studies mainly predicted review helpfulness based on the consistency of text and rating information of online reviews. However, there is a limitation in that representation capacity or review text and rating interaction. We propose a CNN-RHP model that effectively learns the interaction between review text and rating information to improve the limitations of previous studies. Multi-channel CNNs were applied to extract the semantic representation of the review text. We also converted rating into independent high-dimensional embedding vectors representing the same dimension as the text vector. The consistency between the review text and the rating information is learned based on element-wise operations between the review text and the star rating vector. To evaluate the performance of the proposed CNN-RHP model in this study, we used online reviews collected from Amazom.com. Experimental results show that the CNN-RHP model indicates excellent performance compared to several benchmark models. The results of this study can provide practical implications when providing services related to review helpfulness on online e-commerce platforms.
Keywords
Online Reviews; Review Helpfulness; Review Text; Multi-channel CNN;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Mohammad, A. H., Alwada'n, T., & Al-Momani, O. (2016). Arabic text categorization using support vector machine, Naive Bayes and neural network. GSTF Journal on Computing, 5(1), 1-8.   DOI
2 Mudambi, S. M., & Schuff, D. (2010). Research note: What makes a helpful online review? A study of customer reviews on Amazon. com. MIS Quarterly, 34(1),185-200.   DOI
3 Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. (2012). A literature review and classification of recommender systems research. Expert Systems with Applications, 39(11), 10059-10072.   DOI
4 Qu, X., Li, X., & Rose, J. R. (2018). Review helpfulness assessment based on convolutional neural network. arXiv preprint arXiv:1808.09016.
5 Quaschning, S., Pandelaere, M., & Vermeir, I. (2015). When consistency matters: The effect of valence consistency on review helpfulness. Journal of Computer-Mediated Communication, 20(2), 136-152.   DOI
6 Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30-40.   DOI
7 Saumya, S., Singh, J. P., & Dwivedi, Y. K. (2020). Predicting the helpfulness score of online reviews using convolutional neural network. Soft Computing, 24(15), 10989-11005.   DOI
8 Tay, W., Zhang, X., & Karimi, S. (2020). Beyond mean rating: Probabilistic aggregation of star ratings based on helpfulness. Journal of the Association for Information Science and Technology, 71(7), 784-799.   DOI
9 Du, J., Zheng, L., He, J., Rong, J., Wang, H., & Zhang, Y. (2020). An interactive network for end-to-end review helpfulness modeling. Data Science and Engineering, 5(3), 261-279.   DOI
10 Park, J., Gu, B., & Lee, H. (2012). The relationship between retailer-hosted and third-party hosted WOM sources and their influence on retailer sales. Electronic Commerce Research and Applications, 11(3), 253-261.   DOI
11 Ghose, A., & Ipeirotis, P. G. (2010). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 23(10), 1498-1512.   DOI
12 Liu, J., Cao, Y., Lin, C.-Y., Huang, Y., & Zhou, M. (2007). Low-quality product review detection in opinion summarization. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2007), 334-342.
13 Hoffait, A.-S., Ittoo, A., & Schyns, M. (2018). Assessing and predicting review helpfulness: Critical review. 29eme Conference Europeenne Sur La Recherche Operationnelle, Valence, Spain.
14 Huang, A. H., Chen, K., Yen, D. C., & Tran, T. P. (2015). A study of factors that contribute to online review helpfulness. Computers in Human Behavior, 48, 17-27.   DOI
15 Jones, Q., Ravid, G., & Rafaeli, S. (2004). Information overload and the message dynamics of online interaction spaces: A theoretical model and empirical exploration. Information Systems Research, 15(2), 194-210.   DOI
16 Kim, S.-M., Pantel, P., Chklovski, T., & Pennacchiotti, M. (2006). Automatically assessing review helpfulness. Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), 423-430.
17 Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), 1746-1751.
18 Han, Q., Kou, Y., & Snaidauf, D. (2019). Experimental Evaluation of CNN Parameters for Text Categorization in Legal Document Review. 2019 IEEE International Conference on Big Data, Los Angeles, USA.
19 Kim, Y., Jernite, Y., Sontag, D., & Rush, A. M. (2016). Character-aware neural language models. Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, USA.
20 Li, Q., Li, X., Lee, B., & Kim, J. (2021). A hybrid CNN-based review helpfulness filtering model for improving e-commerce recommendation Service. Applied Sciences, 11(18), 8613.   DOI
21 Liu, Z., Yuan, B., & Ma, Y. (2021). A multi-task dual attention deep recommendation model using ratings and review helpfulness. Applied Intelligence, 52(5), 1-13.
22 이선영, 정남호, 양성병. (2019). 온라인 리뷰에서 이미지 효용성 결정요인에 관한 탐색적 연구: 음이항 모형 적용. 인터넷전자상거래연구, 19(1), 93-113.
23 Dong, J., He, F., Guo, Y., & Zhang, H. (2020). A commodity review sentiment analysis based on BERT-CNN model. 2020 5th International Conference on Computer and Communication Systems, Coimbatore, India.
24 Siering, M., Muntermann, J., & Rajagopalan, B. (2018). Explaining and predicting online review helpfulness: The role of content and reviewer-related signals. Decision Support Systems, 108, 1-12.   DOI
25 van Dinter, R., Catal, C., & Tekinerdogan, B. (2021). A Multi-Channel Convolutional Neural Network approach to automate the citation screening process. Applied Soft Computing, 112, 107765.   DOI
26 Yang, S., Yao, J., & Qazi, A. (2020). Does the review deserve more helpfulness when its title resembles the content? Locating helpful reviews by text mining. Information Processing & Management, 57(2), 102179.   DOI
27 Yang, Y., Yan, Y., Qiu, M., & Bao, F. (2015). Semantic analysis and helpfulness prediction of text for online product reviews. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015), 38-44
28 Yin, D., Bond, S. D., & Zhang, H. (2014). Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews. MIS Quarterly, 38(2), 539-560.   DOI
29 Yin, D., Mitra, S., & Zhang, H. (2016). Research note-When do consumers value positive vs. negative reviews? An empirical investigation of confirmation bias in online word of mouth. Information Systems Research, 27(1), 131-144.   DOI
30 이청용, 이병현, 이흠철, 김재경. (2021). CNN 기반 리뷰 유용성 점수 예측을 통한 개인화 추천 서비스 성능 향상에 관한 연구. 지능정보연구, 27(3), 29-56.   DOI
31 박호연, 김경재. (2019). CNN-LSTM 조합모델을 이용한 영화리뷰 감성분석. 지능정보연구, 25(4), 141-154.   DOI
32 Charrada, E. B. (2016). Which one to read? Factors influencing the usefulness of online reviews for RE. 2016 IEEE 24th International Requirements Engineering Conference Workshops, Beijing, China.
33 Fang, B., Ye, Q., Kucukusta, D., & Law, R. (2016). Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics. Tourism Management, 52, 498-506.   DOI
34 Chen, H., Han, F. X., Niu, D., Liu, D., Lai, K., Wu, C., & Xu, Y. (2018). Mix: Multi-channel information crossing for text matching. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (ACM 2018), 110-119.
35 Du, J., Rong, J., Wang, H., & Zhang, Y. (2021). Neighbor-aware review helpfulness prediction. Decision Support Systems, 148, 113581.   DOI
36 Fan, M., Feng, Y., Sun, M., Li, P., Wang, H., & Wang, J. (2018). Multi-task neural learning architecture for end-to-end identification of helpful reviews. 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Barcelona, Spain.
37 He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T.-S. (2017). Neural collaborative filtering. Proceedings of the 26th International Conference on World Wide Web (WWW 2017), 173-182.
38 Malik, M., & Hussain, A. (2018). An analysis of review content and reviewer variables that contribute to review helpfulness. Information Processing & Management, 54(1), 88-104.   DOI
39 Mitra, S., & Jenamani, M. (2021). Helpfulness of online consumer reviews: A multi-perspective approach. Information Processing & Management, 58(3), 102538.   DOI
40 이승우, 강경모, 이병현, 이청용, 김재경. (2022). 사용자의 정성적 선호도와 정량적 선호도를 고려하는 추천 시스템 성능 향상에 관한 연구. 경영과학, 39(1), 15-27.
41 전민진, 황지원, 김종우. (2021). CNN 보조 손실을 이용한 차원 기반 감성 분석. 지능정보연구, 27(4), 1-22.
42 Diaz, G. O., & Ng, V. (2018). Modeling and prediction of online product review helpfulness: a survey. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), 698-708.