Context-Aware Helpfulness Prediction for Online Product Reviews

  • Iyiola E. OlatunjiEmail author
  • Xin Li
  • Wai Lam
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12004)


Modeling and prediction of review helpfulness has become more predominant due to proliferation of e-commerce websites and online shops. Since the functionality of a product cannot be tested before buying, people often rely on different kinds of user reviews to decide whether or not to buy a product. However, quality reviews might be buried deep in the heap of a large amount of reviews. Therefore, recommending reviews to customers based on the review quality is of the essence. Since there is no direct indication of review quality, most reviews use the information that “X out of Y” users found the review helpful for obtaining the review quality. However, this approach undermines helpfulness prediction because not all reviews have statistically abundant votes. In this paper, we propose a neural deep learning model that predicts the helpfulness score of a review. This model is based on convolutional neural network (CNN) and a context-aware encoding mechanism which can directly capture relationships between words irrespective of their distance in a long sequence. We validated our model on human annotated dataset and the result shows that our model significantly outperforms existing models for helpfulness prediction.


Helpfulness prediction Context-aware Product review 


  1. 1.
    Ambartsoumian, A., Popowich, F.: Self-attention: a better building block for sentiment analysis neural network classifiers. In: Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 130–139 (2018)Google Scholar
  2. 2.
    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Proceedings of ICLR, pp. 1–15 (2015)Google Scholar
  3. 3.
    Chen, C., et al.: Review Helpfulness Prediction with Embedding-Gated CNN. arXiv (2018)Google Scholar
  4. 4.
    Chen, C., et al.: Multi-domain gated CNN for review helpfulness prediction. In: Proceedings of WWW, pp. 2630–2636 (2019)Google Scholar
  5. 5.
    Chen, C., Yang, Y., Zhou, J., Li, X., Bao, F.S.: Cross-domain review helpfulness prediction based on convolutional neural networks with auxiliary domain discriminators. In: Proceedings of NAACL-HLT, pp. 602–607 (2018)Google Scholar
  6. 6.
    Diaz, G.O., Ng, V.: Modeling and prediction of online product review helpfulness: a survey. In: Proceedings of ACL, pp. 698–708 (2018)Google Scholar
  7. 7.
    Duan, W., Gu, B., Whinston, A.B.: The dynamics of online word-of-mouth and product sales - an empirical investigation of the movie industry. J. Retail. 84, 233–242 (2008)CrossRefGoogle Scholar
  8. 8.
    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(10), 1498–1512 (2011)CrossRefGoogle Scholar
  9. 9.
    Kim, S.M., Pantel, P., Chklovski, T., Pennacchiotti, M.: Automatically assessing review helpfulness. In: Proceedings of EMNLP, pp. 423–430 (2006)Google Scholar
  10. 10.
    Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of EMNLP, pp. 1746–1751 (2014)Google Scholar
  11. 11.
    Lee, S., Choeh, J.Y.: Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Syst. Appl. 41(6), 3041–3046 (2014)CrossRefGoogle Scholar
  12. 12.
    Liu, H., et al.: Using argument-based features to predict and analyse review helpfulness. In: Proceedings of EMNLP, pp. 1358–1363 (2017)Google Scholar
  13. 13.
    Lu, Y., Tsaparas, P., Ntoulas, A., Polanyi, L.: Exploiting social context for review quality prediction. In: Proceedings of WWW, pp. 691–700 (2010)Google Scholar
  14. 14.
    Martin, L., Pu, P.: Prediction of helpful reviews using emotions extraction. In: Proceedings of AAAI, pp. 1551–1557 (2014)Google Scholar
  15. 15.
    Mcauley, J., Targett, C., Hengel, A.V.D.: Image-based recommendations on styles and substitutes. In: Proceedings of SIGIR (2015)Google Scholar
  16. 16.
    Moghaddam, S., Jamali, M., Ester, M.: ETF: extended tensor factorization model for personalizing prediction of review helpfulness. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 163–172 (2012)Google Scholar
  17. 17.
    Mudambi, S.M., Schuff, D.: Research note: what makes a helpful online review? A study of customer reviews on MIS Quart. 34(1), 185–200 (2010)CrossRefGoogle Scholar
  18. 18.
    Mukherjee, S., Popat, K., Weikum, G.: Exploring latent semantic factors to find useful product reviews. In: Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 480–488 (2017)CrossRefGoogle Scholar
  19. 19.
    Otterbacher, J.: ‘Helpfulness’ in online communities: a measure of message quality. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 955–964 (2009)Google Scholar
  20. 20.
    Pan, Y., Zhang, J.Q.: Born unequal: a study of the helpfulness of user-generated product reviews. J. Retail. 87(4), 598–612 (2011)CrossRefGoogle Scholar
  21. 21.
    Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Proceedings of EMNLP, pp. 1532–1543 (2014)Google Scholar
  22. 22.
    Salehan, M., Kim, D.J.: Predicting the performance of online consumer reviews: a sentiment mining approach to big data analytics. Decis. Support Syst. 81, 30–40 (2016)CrossRefGoogle Scholar
  23. 23.
    Tang, J., Gao, H., Hu, X., Liu, H.: Context-aware review helpfulness rating prediction. In: Proceedings of the 7th ACM Conference on Recommender Systems (2013)Google Scholar
  24. 24.
    Vaswani, A., et al.: Attention is all you need. In: Proceedings of NIPS (2017)Google Scholar
  25. 25.
    Yang, Y., Chen, C., Bao, F.S.: Aspect-based helpfulness prediction for online product reviews. In: Proceedings of International Conference on Tools with Artificial Intelligence (ICTAI), pp. 836–843 (2016).
  26. 26.
    Yang, Y., Yan, Y., Qiu, M., Bao, F.S.: Semantic analysis and helpfulness prediction of text for online product reviews. In: Proceedings of ACL-IJCNLP, pp. 38–44 (2015)Google Scholar
  27. 27.
    Yin, W., Schutze, H., Xiang, B., Zhou, B.: ABCNN: attention-based convolutional neural network for modeling sentence pairs. Trans. Assoc. Comput. Linguist. 4, 259–272 (2016)CrossRefGoogle Scholar
  28. 28.
    Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Proceedings of NIPS, pp. 649–657 (2015)Google Scholar
  29. 29.
    Zhang, Z., Varadarajan, B.: Utility scoring of product reviews. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, pp. 51–57 (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Systems Engineering and Engineering ManagementThe Chinese University of Hong KongShatinHong Kong

Personalised recommendations