Abstract
Online user generated reviews are now a vital source of product evaluation to both consumer and retailer. There is a need of knowing the factors, generally affecting the helpfulness of reviews and how to identify them. Various studies and researches have been conducted for finding the helpfulness value of online reviews in past recent years. In this paper we have summarized and then analyzed the past work of review helpfulness prediction in a systematic way. The paper provides brief of methodology of each study and how it is contributing towards this domain. It also emphasizes on the pros of the methods used in past and how they are lacking in determining few other aspects of the review helpfulness. The survey discovers that the most popular techniques used for helpfulness prediction are supervised ones and most frequently used are Regression Models and SVM.
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References
Casalo, L.V., Flavián, C., Guinalíu, M.: Understanding the intention to follow the advice obtained in an online travel community. Comput. Hum. Behav. 27(2), 622–633 (2011)
Zhu, F., Zhang, X.: Impact of online consumer reviews on sales: the moderating role of product and consumer characteristics. J. Mark. 74(2), 133–148 (2010)
Lee, J., Park, D.H., Han, I.: The effect of negative online consumer reviews on product attitude: an information processing view. Electron. Commer. Res. Appl. 7(3), 341–352 (2008)
Cao, Q., Duan, W., Gan, Q.: Exploring determinants of voting for the “helpfulness” of online user reviews: a text mining approach. Decis. Support Syst. 50, 511–521 (2011)
Ghose, A., Ipeirotis, P.G., Li, B.: Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Mark. Sci. 31, 493–520 (2012)
Zhang, Y., Zhang, D.: Automatically predicting the helpfulness of online reviews. In: 2014 IEEE 15th International Conference on Information Reuse and Integration (IRI), pp. 662–668, August 2014
Zhang, Z., Qi, J., Zhu, G.: Mining customer requirement from helpful online reviews. In: Enterprise Systems Conference (ES), pp. 249–254, August 2014
Chen, Y., Chai, Y., Liu, Y., Xu, Y.: Analysis of review helpfulness based on consumer perspective. Tsinghua Sci. Technol. 20, 293–305 (2015)
Chen, J., Zhang, C., Niu, Z.: Identifying helpful online reviews with word embedding features. In: International Conference on Knowledge Science, Engineering and Management, pp. 123–133, October 2016
Singh, J.P., Irani, S., Rana, N.P., Dwivedi, Y.K., Saumya, S., Roy, P.K.: Predicting the “helpfulness” of online consumer reviews. J. Bus. Res. 70, 346–355 (2017)
Chua, A.Y., Banerjee, S.: Helpfulness of user-generated reviews as a function of review sentiment, product type and information quality. Comput. Hum. Behav. 54, 547–554 (2016)
Krishnamoorthy, S.: Linguistic features for review helpfulness prediction. Expert Syst. Appl. 42, 3751–3759 (2015)
Ullah, R., Zeb, A., Kim, W.: The impact of emotions on the helpfulness of movie reviews. J. Appl. Res. Technol. 13, 359–363 (2015)
Qazi, A., Syed, K.B.S., Raj, R.G., Cambria, E., Tahir, M., Alghazzawi, D.: A concept-level approach to the analysis of online review helpfulness. Comput. Hum. Behav. 58, 75–81 (2016)
Zhang, Z., Wei, Q., Chen, G.: Estimating online review helpfulness with probabilistic distribution and confidence. In: Foundations and Applications of Intelligent Systems, pp. 411–420 (2014)
Thuan, T.T., Puntheeranurak, S.: Hybrid recommender system with review helpfulness features. In: TENCON 2014-2014 IEEE Region 10 Conference, pp. 1–5, October 2014
Lee, S., Choeh, J.Y.: Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Syst. Appl. 41, 3041–3046 (2014)
Ngo-Ye, T.L., Sinha, A.P.: The influence of reviewer engagement characteristics on online review helpfulness: a text regression model. Decis. Support Syst. 61, 47–58 (2014)
Wan, Y., Nakayama, M.: The reliability of online review helpfulness. J. Electron. Commer. Res. 15, 179 (2014)
Karimi, S., Wang, F.: Online review helpfulness: impact of reviewer profile image. Decis. Support Syst. 96, 39–48 (2014)
Ghose, A., Ipeirotis, P.G.: Estimating the helpfulness and economic impact of product reviews: mining text and reviewer characteristics. IEEE Trans. Knowl. Data Eng. 23, 1498–1512 (2011)
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Arif, M., Qamar, U., Khan, F.H., Bashir, S. (2019). A Survey of Customer Review Helpfulness Prediction Techniques. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-030-01054-6_15
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DOI: https://doi.org/10.1007/978-3-030-01054-6_15
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