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Predicting the “helpfulness” of online consumer reviews

Author

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  • Singh, Jyoti Prakash
  • Irani, Seda
  • Rana, Nripendra P.
  • Dwivedi, Yogesh K.
  • Saumya, Sunil
  • Kumar Roy, Pradeep

Abstract

Online shopping is increasingly becoming people's first choice when shopping, as it is very convenient to choose products based on their reviews. Even for moderately popular products, there are thousands of reviews constantly being posted on e-commerce sites. Such a large volume of data constantly being generated can be considered as a big data challenge for both online businesses and consumers. That makes it difficult for buyers to go through all the reviews to make purchase decisions. In this research, we have developed models based on machine learning that can predict the helpfulness of the consumer reviews using several textual features such as polarity, subjectivity, entropy, and reading ease. The model will automatically assign helpfulness values to an initial review as soon as it is posted on the website so that the review gets a fair chance of being viewed by other buyers. The results of this study will help buyers to write better reviews and thereby assist other buyers in making their purchase decisions, as well as help businesses to improve their websites.

Suggested Citation

  • Singh, Jyoti Prakash & Irani, Seda & Rana, Nripendra P. & Dwivedi, Yogesh K. & Saumya, Sunil & Kumar Roy, Pradeep, 2017. "Predicting the “helpfulness” of online consumer reviews," Journal of Business Research, Elsevier, vol. 70(C), pages 346-355.
    • Handle: RePEc:eee:jbrese:v:70:y:2017:i:c:p:346-355
    DOI: 10.1016/j.jbusres.2016.08.008
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    Cited by:

    1. Pradeep Kumar Roy & Zishan Ahmad & Jyoti Prakash Singh & Mohammad Abdallah Ali Alryalat & Nripendra P. Rana & Yogesh K. Dwivedi, 2018. "Finding and Ranking High-Quality Answers in Community Question Answering Sites," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 19(1), pages 53-68, March.
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    4. Kaushik, Kapil & Mishra, Rajhans & Rana, Nripendra P. & Dwivedi, Yogesh K., 2018. "Exploring reviews and review sequences on e-commerce platform: A study of helpful reviews on Amazon.in," Journal of Retailing and Consumer Services, Elsevier, vol. 45(C), pages 21-32.
    5. Candi, Marina & Jae, Haeran & Makarem, Suzanne & Mohan, Mayoor, 2017. "Consumer responses to functional, aesthetic and symbolic product design in online reviews," Journal of Business Research, Elsevier, vol. 81(C), pages 31-39.
    6. Sharapudinov, S. & Zezerova, V. & Storchevoy, M., 2017. "Determinants of Online Word-of-Mouth: Evidence from Durable Goods Market," Working Papers 8721, Graduate School of Management, St. Petersburg State University.
    7. Samira FRIOUI & Amel GRAA, 2024. "Bibliometric Analysis of Artificial Intelligence in the Scope of E-Commerce: Trends and Progress over the Last Decade," Management and Economics Review, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 9(1), pages 5-24, February.
    8. Chen, Feier & Liu, Stephanie Q. & Mattila, Anna S., 2020. "Bragging and humblebragging in online reviews," Annals of Tourism Research, Elsevier, vol. 80(C).
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    10. Moon, Sangkil & Kim, Moon-Yong & Bergey, Paul K., 2019. "Estimating deception in consumer reviews based on extreme terms: Comparison analysis of open vs. closed hotel reservation platforms," Journal of Business Research, Elsevier, vol. 102(C), pages 83-96.
    11. Chen, Lele & Jing, Kunpeng & Mei, Yupeng, 2024. "The effect of consumption goals on review helpfulness: Behavioral and eye-tracking research," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    12. Müller, Steffen & Beinert, Markus & Struik, Arie, 2017. "Welche Produkt­eigenschaften begeistern Kunden? - Eine Analyse von Online Reviews," Marketing Review St.Gallen, Universität St.Gallen, Institut für Marketing und Customer Insight, vol. 34(1), pages 68-74.
    13. Yi, Jisu & Oh, Yun Kyung, 2022. "The informational value of multi-attribute online consumer reviews: A text mining approach," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    14. Jeremy K. Nguyen & Adam Karg & Abbas Valadkhani & Heath McDonald, 2022. "Predicting individual event attendance with machine learning: a ‘step-forward’ approach," Applied Economics, Taylor & Francis Journals, vol. 54(27), pages 3138-3153, June.
    15. Ismagilova, Elvira & Dwivedi, Yogesh K. & Slade, Emma, 2020. "Perceived helpfulness of eWOM: Emotions, fairness and rationality," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    16. Zhu, Yongmin & Liu, Miaomiao & Zeng, Xiaohua & Huang, Pei, 2020. "The effects of prior reviews on perceived review helpfulness: A configuration perspective," Journal of Business Research, Elsevier, vol. 110(C), pages 484-494.
    17. Bag, Sujoy & Tiwari, Manoj Kumar & Chan, Felix T.S., 2019. "Predicting the consumer's purchase intention of durable goods: An attribute-level analysis," Journal of Business Research, Elsevier, vol. 94(C), pages 408-419.
    18. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
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