IRHM: Inclusive Review Helpfulness Model for Review Helpfulness Prediction in E-commerce Platform

Document Type: Proceedings of The 6'th International Conference on Communication Management and Information Technology (ICCMIT'20)

Authors

1 Master student in Information system at king Abduziz University, Jeddah, Saudi Arabia.

2 Associated Professor in king Abuduziz University, Jeddah, Saudi Arabia.

Abstract

Online reviews have become essential aspect in E-commerce platforms due to its role for assisting customers’ buying choices. Furthermore, the most helpful reviews that have some attributes are support customers buying decision; therefore, there is needs for investigating what are the attributes that increase the Review Helpfulness (RH). This research paper proposed novel model called inclusive review helpfulnessmodel (IRHM) can be used to detect the most attributes affecting the RH and build classifier that can predict RH based on these attributes. IRHM is implemented on Amazon.com using collection of reviews from different categories. The results show that IRHM can detect the most important attributes and classify the reviews as helpful or not with accuracy of 94%, precision of 0.20 and had excellent area under curve close to 0.94.

Keywords


Ahmed, E., Sazzad, M. A. U., Islam, M. T., Azad, M., Islam, S., & Ali, M. H. (2017). Challenges, comparative analysis and a proposed methodology to predict sentiment from movie reviews using machine learning. Paper presented at the Big Data Analytics and Computational Intelligence (ICBDAC), 2017 International Conference on.
Baek, H., Ahn, J., & Choi, Y. (2012). Helpfulness of online consumer reviews: Readers' objectives and review cues. International Journal of Electronic Commerce, 17(2), 99-126.
Barbosa, J. L., Moura, R. S., & Santos, R. L. d. S. (2016). Predicting Portuguese Steam Review Helpfulness Using Artificial Neural Networks. Paper presented at the Proceedings of the 22nd Brazilian Symposium on Multimedia and the Web.
Cao, Q., Duan, W., & Gan, Q. (2011). Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems, 50(2), 511-521.
Eibe Frank, M. A. H., and Ian H. Witten. (2016). h. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Fourth Edition,.
Esmailian. (2019). when to use Standard Scaler and when Normalizer? stackExchange. Retrieved from https://datascience.stackexchange.com/questions/45900/when-to-use-standard-scaler-and-when-normalizer
Filieri, R. (2015). What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. Journal of Business Research, 68(6), 1261-1270.
Gao, B., Hu, N., & Bose, I. (2017). Follow the herd or be myself? An analysis of consistency in behavior of reviewers and helpfulness of their reviews. Decision Support Systems, 95, 1-11.
Ghose, A., & Ipeirotis, P. G. (2011). 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.
Goswami, K., Park, Y., & Song, C. (2017). Impact of reviewer social interaction on online consumer review fraud detection. Journal of Big Data, 4(1), 15.
Hong, H., Xu, D., Wang, G. A., & Fan, W. (2017). Understanding the determinants of online review helpfulness: A meta-analytic investigation. Decision Support Systems, 102, 1-11.
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2000). Introduction to the logistic regression model. Applied logistic regression, 2, 1-30.
Karimi, S., & Wang, F. (2017). Online review helpfulness: Impact of reviewer profile image. Decision Support Systems, 96, 39-48.
Kim, S.-M., Pantel, P., Chklovski, T., & Pennacchiotti, M. (2006). Automatically assessing review helpfulness. Paper presented at the Proceedings of the 2006 Conference on empirical methods in natural language processing.
Krishnamoorthy, S. (2015). Linguistic features for review helpfulness prediction. Expert Systems with Applications, 42(7), 3751-3759.
Li, M., Huang, L., Tan, C.-H., & Wei, K.-K. (2013). Helpfulness of online product reviews as seen by consumers: Source and content features. International Journal of Electronic Commerce, 17(4), 101-136.
Loria, S. (2018). TextBlob: Simplified Text Processing. Retrieved from https://textblob.readthedocs.io/en/dev/#
Moore, S. G. (2015). Attitude predictability and helpfulness in online reviews: The role of explained actions and reactions. Journal of Consumer Research, 42(1), 30-44.
Myers, D., & McGuffee, J. W. (2015). Choosing scrapy. Journal of Computing Sciences in Colleges, 31(1), 83-89.
Ngo-Ye, T. L., & Sinha, A. P. (2014). The influence of reviewer engagement characteristics on online review helpfulness: A text regression model. Decision Support Systems, 61, 47-58.
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.
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.
Wu, J. (2017). Review popularity and review helpfulness: A model for user review effectiveness. Decision Support Systems, 97, 92-103.
Yin, D., Bond, S., & Zhang, H. (2013). Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews.
Zhang, Y., & Zhang, D. (2014). Automatically predicting the helpfulness of online reviews. Paper presented at the Information Reuse and Integration (IRI), 2014 IEEE 15th International Conference on.