Predicting the Usefulness of E-Commerce Products’ Reviews using Machine Learning Techniques

Dimple Chehal, Parul Gupta, Payal Gulati

Abstract


User-generated reviews are an essential component of e-commerce platforms. The presence of a large number of these reviews creates an information overload problem, making it difficult for other users to establish their purchase decision. A review voting mechanism, in which users can vote for or against a review, addresses this issue (as helpful or not). The helpful votes on a review reflect its usefulness to other users. As voting on usefulness is optional, not all reviews receive this vote. Furthermore, reviews posted recently by users are not associated with any vote (s). The aim of this paper is to predict the usefulness of user reviews through machine learning techniques. Using the Amazon product review dataset of cell phones, classification models are built on eight features and compared on seven performance measures. As per results, all the classification models performed well, except Linear Discriminant Analysis. The classification performance of Logistic Regression, Decision Tree, Random Forest, Ada Boost, and Gradient Boost was unaffected by feature selection or outlier removal. The performance of Linear Discriminant Analysis improved after feature selection but decreased after outlier removal, whereas ET and KNN classifiers improved in both cases.


Full Text:

PDF

References


Ahmad, S.N. and Laroche, M. 2017. Analyzing electronic word of mouth: A social commerce construct. International Journal of Information Management. 37, 3 (Jun. 2017), 202–213. DOI:https://doi.org/10.1016/J.IJINFOMGT.2016.08.004.

Akbarabadi, M. and Hosseini, M. 2020. Predicting the helpfulness of online customer reviews: The role of title features. International Journal of Market Research. 62, 3 (2020), 272–287. DOI:https://doi.org/10.1177/1470785318819979.

Amazon Review Data: 2018. https://jmcauley.ucsd.edu/data/amazon/. Accessed: 2021-05-14.

Ampomah, E.K. et al. 2021. Stock market decision support modeling with tree-based AdaBoost ensemble machine learning models. Informatica. 44, 4 (Mar. 2021), 477–489. DOI:https://doi.org/10.31449/inf.v44i4.3159.

Arif, M. et al. 2019. A Survey of Customer Review Helpfulness Prediction Techniques. Advances in Intelligent Systems and Computing. Springer International Publishing. 215–226.

Bilal, M. et al. 2019. Profiling and predicting the cumulative helpfulness (Quality) of crowdsourced reviews. Information (Switzerland).

Bilal, M. et al. 2021. Profiling reviewers’ social network strength and predicting the “Helpfulness” of online customer reviews. Electronic Commerce Research and Applications. 45, (Jan. 2021), 101026. DOI:https://doi.org/10.1016/j.elerap.2020.101026.

Chen, C. et al. 2019. Multi-domain gated CNn for review helpfulness prediction. The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019. (2019), 2630–2636. DOI:https://doi.org/10.1145/3308558.3313587.

Chua, A.Y.K. and Banerjee, S. 2016. Helpfulness of user-generated reviews as a function of review sentiment, product type and information quality. Computers in Human Behavior. 54, (2016), 547–554. DOI:https://doi.org/10.1016/j.chb.2015.08.057.

Dey, D. and Kumar, P. 2019. A novel approach to identify the determinants of online review helpfulness and predict the helpfulness score across product categories. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer. 365–388.

Du, J. et al. 2021. Neighbor-aware review helpfulness prediction. Decision Support Systems. April (2021), 113581. DOI:https://doi.org/10.1016/j.dss.2021.113581.

Enamul Haque, M. et al. 2018. Helpfulness prediction of online product reviews. Proceedings of the ACM Symposium on Document Engineering 2018, DocEng 2018. (2018). DOI:https://doi.org/10.1145/3209280.3229105.

Eslami, S.P. et al. 2018. Which online reviews do consumers find most helpful? A multi-method investigation. Decision Support Systems. 113, (Sep. 2018), 32–42. DOI:https://doi.org/10.1016/J.DSS.2018.06.012.

Fan, M. et al. 2019. Product-aware helpfulness prediction of online reviews. The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019. 2, Ccl (2019), 2715–2721. DOI:https://doi.org/10.1145/3308558.3313523.

Fink, L. et al. 2018. Longer online reviews are not necessarily better. International Journal of Information Management. 39, (Apr. 2018), 30–37. DOI:https://doi.org/10.1016/J.IJINFOMGT.2017.11.002.

Ge, S. et al. 2019. Helpfulness-aware review based neural recommendation. CCF Transactions on Pervasive Computing and Interaction. 1, 4 (Dec. 2019), 285–295. DOI:https://doi.org/10.1007/s42486-019-00023-0.

Hamad et al. 2018. Review helpfulness as a function of Linguistic Indicators. IJCSNS International Journal of Computer Science and Network Security. 18, 1 (2018), 234–240.

Hong, H. et al. 2017. Understanding the determinants of online review helpfulness: A meta-analytic investigation. Decision Support Systems. 102, (2017), 1–11. DOI:https://doi.org/10.1016/j.dss.2017.06.007.

Kaddoura, S. et al. 2022. A systematic review on machine learning models for online learning and examination systems. PeerJ Computer Science. 8, (May 2022), e986. DOI:https://doi.org/10.7717/PEERJ-CS.986.

Kong, L. et al. 2022. Predicting Product Review Helpfulness - A Hybrid Method. IEEE Transactions on Services Computing. 15, 4 (2022), 2213–2225. DOI:https://doi.org/10.1109/TSC.2020.3041095.

Krishnamoorthy, S. 2015. Linguistic features for review helpfulness prediction. Expert Systems with Applications. 42, 7 (May 2015), 3751–3759. DOI:https://doi.org/10.1016/J.ESWA.2014.12.044.

Liu, A.X. et al. 2019. It’s Not Just What You Say, But How You Say It: The Effect of Language Style Matching on Perceived Quality of Consumer Reviews. Journal of Interactive Marketing. 46, (May 2019), 70–86. DOI:https://doi.org/10.1016/J.INTMAR.2018.11.001.

Luo, Y. and Xu, X. 2019. Predicting the helpfulness of online restaurant reviews using different machine learning algorithms: A case study of yelp. Sustainability (Switzerland). 11, 19 (2019). DOI:https://doi.org/10.3390/su11195254.

Malik, M.S.I. 2020. Predicting users’ review helpfulness: the role of significant review and reviewer characteristics. Soft Computing. 24, 18 (Sep. 2020), 13913–13928. DOI:https://doi.org/10.1007/s00500-020-04767-1.

Malik, M.S.I. and Hussain, A. 2018. An analysis of review content and reviewer variables that contribute to review helpfulness. Information Processing and Management. 54, 1 (2018), 88–104. DOI:https://doi.org/10.1016/j.ipm.2017.09.004.

Malik, M.S.I. and Hussain, A. 2020. Exploring the influential reviewer, review and product determinants for review helpfulness. Artificial Intelligence Review. 53, 1 (2020), 407–427. DOI:https://doi.org/10.1007/s10462-018-9662-y.

Mauro, N. et al. 2021. User and item-aware estimation of review helpfulness. Information Processing and Management.

Mitra, S. and Jenamani, M. 2021. Helpfulness of online consumer reviews: A multi-perspective approach. Information Processing and Management. 58, 3 (2021), 102538. DOI:https://doi.org/10.1016/j.ipm.2021.102538.

Ni, J. et al. 2020. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference. (2020), 188–197. DOI:https://doi.org/10.18653/v1/d19-1018.

Orimaye, S.O. et al. 2016. Learning Sentiment Dependent Bayesian Network Classifier for Online Product Reviews. Informatica (Slovenia). 40, 2 (2016), 225–235.

Salehan, M. and Kim, D.J. 2016. Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems. 81, (Jan. 2016), 30–40. DOI:https://doi.org/10.1016/J.DSS.2015.10.006.

Saumya, S. et al. 2018. Ranking online consumer reviews. Electronic Commerce Research and Applications. 29, (2018), 78–89. DOI:https://doi.org/10.1016/j.elerap.2018.03.008.

Sidhu, R.K. et al. 2020. Machine learning based crop water demand forecasting using minimum climatological data. Multimedia Tools and Applications. 79, 19–20 (2020), 13109–13124. DOI:https://doi.org/10.1007/s11042-019-08533-w.

Sun, X. et al. 2019. Helpfulness of online reviews: Examining review informativeness and classification thresholds by search products and experience products. Decision Support Systems. 124, (Sep. 2019), 113099. DOI:https://doi.org/10.1016/J.DSS.2019.113099.

Wu, J. 2017. Review popularity and review helpfulness: A model for user review effectiveness. Decision Support Systems. 97, (2017), 92–103. DOI:https://doi.org/10.1016/j.dss.2017.03.008.

Yenkikar, A. et al. 2022. Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble. PeerJ Computer Science. 8, (Sep. 2022), e1100. DOI:https://doi.org/10.7717/PEERJ-CS.1100.




DOI: https://doi.org/10.31449/inf.v47i2.4155

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.