Skip to main content
SpringerLink
Log in
Book cover

Future of Information and Communication Conference

FICC 2020: Advances in Information and Communication pp 270–281Cite as

BERT Feature Based Model for Predicting the Helpfulness Scores of Online Customers Reviews

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1130))

Abstract

Online product reviews help consumers make purchase decisions when shopping online. As such, many computational models have been constructed to automatically evaluate the helpfulness of customer product reviews. However, many existing models are based on simple explanatory variables, including those extracted from low quality reviews that can be misleading and lead to confusion. Quality feature selection is essential for predicting the helpfulness of online customer reviews. The Bidirectional Encoder Representations from Transformers (BERT) is a very recently developed language representation model which can attain state-of-the-art results on many natural language processing tasks. In this study, a predictive model for determining helpfulness scores of customer reviews based on incorporation of BERT features with deep learning techniques is proposed. The application analyzes the Amazon product reviews dataset, and uses a BERT features based algorithm expected to be useful in help consumers to make a better purchase decisions.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Liu, Y., Huang, X., An, A., Yu, X.: Modeling and predicting the helpfulness of online reviews. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 443–452. IEEE (2008)

    Google Scholar 

  2. 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. ACM (2006)

    Google Scholar 

  3. Mudambi, S.M., Schuff, D.: What makes a helpful review? A study of customer reviews on Amazon.com. MIS Q. 34(1), 185–200 (2010)

    Article  Google Scholar 

  4. Park, Y.J.: Predicting the helpfulness of online customer reviews across different product types. Sustainability 10(6), 1735 (2018)

    Article  Google Scholar 

  5. Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th International Conference on World Wide Web, pp. 519–528. ACM (2003)

    Google Scholar 

  6. Read, J.: Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: Proceedings of the ACL Student Research Workshop, pp. 43–48. Association for Computational Linguistics (2005)

    Google Scholar 

  7. Ye, Q., Zhang, Z., Law, R.: Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Syst. Appl. 36(3), 6527–6535 (2009)

    Article  Google Scholar 

  8. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:181004805 (2018)

  9. Xu, H., Liu, B., Shu, L., Yu, P.S.: BERT post-training for review reading comprehension and aspect-based sentiment analysis. arXiv preprint arXiv:190402232 (2019)

  10. Eisend, M.: Two-sided advertising: a meta-analysis. Int. J. Res. Mark. 23(2), 187–198 (2006)

    Article  Google Scholar 

  11. Pavlou, P.A., Dimoka, A.: The nature and role of feedback text comments in online marketplaces: implications for trust building, price premiums, and seller differentiation. Inf. Syst. Res. 17(4), 392–414 (2006)

    Article  Google Scholar 

  12. Forman, C., Ghose, A., Wiesenfeld, B.: Examining the relationship between reviews and sales: the role of reviewer identity disclosure in electronic markets. Inf. Syst. Res. 19(3), 291–313 (2008)

    Article  Google Scholar 

  13. Nelson, P.: Information and consumer behavior. J. Polit. Econ. 78(2), 311–329 (1970)

    Article  Google Scholar 

  14. Ford, G.T., Smith, D.B., Swasy, J.L.: Consumer skepticism of advertising claims: testing hypotheses from economics of information. J. Consum. Res. 16(4), 433–441 (1990)

    Article  Google Scholar 

  15. Pan, Y., Zhang, J.Q.: Born unequal: a study of the helpfulness of user-generated product reviews. J. Retail. 87(4), 598–612 (2011)

    Article  Google Scholar 

  16. 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 (2010)

    Article  Google Scholar 

  17. Korfiatis, N., García-Bariocanal, E., Sánchez-Alonso, S.: Evaluating content quality and helpfulness of online product reviews: the interplay of review helpfulness vs. review content. Electron. Commer. Res. Appl. 11(3), 205–217 (2012)

    Article  Google Scholar 

  18. 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(2), 511–521 (2011)

    Article  Google Scholar 

  19. Kuan, K.K., Hui, K.L., Prasarnphanich, P., Lai, H.Y.: What makes a review voted? An empirical investigation of review voting in online review systems. J. Assoc. Inf. Syst. 16(1), 48 (2015)

    Google Scholar 

  20. Yin, D., Bond, S., Zhang, H.: Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews. MIS Q. 38(2), 539–560 (2014)

    Article  Google Scholar 

  21. Ahmad, S.N., Laroche, M.: How do expressed emotions affect the helpfulness of a product review? Evidence from reviews using latent semantic analysis. Int. J. Electron. Commer. 20(1), 76–111 (2015)

    Article  Google Scholar 

  22. Ma, Y., Xiang, Z., Du, Q., Fan, W.: Effects of user-provided photos on hotel review helpfulness: an analytical approach with deep leaning. Int. J. Hosp. Manag. 71, 120–131 (2018)

    Article  Google Scholar 

  23. 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 the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Short Papers), vol. 2, pp. 602–607 (2018)

    Google Scholar 

  24. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)

    Google Scholar 

  25. Peters, M.E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., Zettlemoyer, L.: Deep contextualized word representations. arXiv preprint arXiv:180205365 (2018)

  26. Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. arXiv preprint arXiv:180106146 (2018)

  27. Bragge, J., Storgårds, J.: Utilizing text-mining tools to enrich traditional literature reviews. case: digital games. In: Proceedings of the 30th Information Systems Research Seminar in Scandinavia IRIS, pp. 1–24 (2007)

    Google Scholar 

  28. Weathers, D., Sharma, S., Wood, S.L.: Effects of online communication practices on consumer perceptions of performance uncertainty for search and experience goods. J. Retail. 83(4), 393–401 (2007)

    Article  Google Scholar 

Download references

Acknowledgment

The authors are indebted to anonymous reviewers for providing constructive comments and suggestions which has resulted in improvement both the readability and quality of the paper.

Author information

Authors and Affiliations

  1. Computational Science Program, Middle Tennessee State University, Murfreesboro, TN, 37132, USA

    Shuzhe Xu, Salvador E. Barbosa & Don Hong

Authors
  1. Shuzhe Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Salvador E. Barbosa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Don Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuzhe Xu .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xu, S., Barbosa, S.E., Hong, D. (2020). BERT Feature Based Model for Predicting the Helpfulness Scores of Online Customers Reviews. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication. FICC 2020. Advances in Intelligent Systems and Computing, vol 1130. Springer, Cham. https://doi.org/10.1007/978-3-030-39442-4_21

Download citation

Publish with us

Policies and ethics

Search

Navigation