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A Novel Approach to Identify the Determinants of Online Review Helpfulness and Predict the Helpfulness Score Across Product Categories

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Big Data Analytics (BDA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11932))

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Abstract

The proliferation in the number of available online reviews provides an excellent opportunity to use this accumulated enormous information of any product in a more strategic way to improve the quality of the product and services of the e-commerce company. Due to the non-uniform quality of online reviews, it is crucial to identify those helpful reviews from the pile of a large amount of low quality and low informative other reviews. This system will help the customers to form an unbiased opinion quickly by looking at its level of helpfulness. The e-commerce companies measure the helpfulness of a review using the number of votes it gets from other customers. This situation arises problems to newly-authored potentially helpful reviews due to lack of votes. Thus it is essential to have an automated process to estimate and predict helpfulness of any review. This paper identifies the essential characteristics of online reviews influencing the helpfulness of it. This study categorized all characteristics of reviews collected from previous literature in four main categories and then study the combined effect of the four aspects in predicting the helpfulness of a review. The product type (Search or Experience) acts as a control variable in the factors identification model of helpful prediction of a review. An analysis of total 14782 reviews from Amazon.com across five different product category shows the factors influencing the helpfulness of a review varies across product categories. Then a comparative study of two widely used machine learning, Artificial Neural Network and Multiple Adaptive Regression Spline are presented to predict the helpfulness of online review across five different categories and a better method of predicting helpfulness of online reviews are suggested based on the type of product. This study solves the starvation problem of potential newly-authored or infamous reviews without any manual votes along with high accuracy of helpfulness prediction.

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Authors and Affiliations

  1. Indian Institute of Management Lucknow, Lucknow, UP, India

    Debasmita Dey & Pradeep Kumar

Authors
  1. Debasmita Dey
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  2. Pradeep Kumar
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Correspondence to Debasmita Dey .

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Editors and Affiliations

  1. Missouri University of Science and Technology, Rolla, MO, USA

    Sanjay Madria

  2. Harbin Institute of Technology, Shenzhen, China

    Philippe Fournier-Viger

  3. Ahmedabad University, Ahmedabad, India

    Sanjay Chaudhary

  4. International Institute of Information Technology, Hyderabad, India

    P. Krishna Reddy

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Dey, D., Kumar, P. (2019). A Novel Approach to Identify the Determinants of Online Review Helpfulness and Predict the Helpfulness Score Across Product Categories. In: Madria, S., Fournier-Viger, P., Chaudhary, S., Reddy, P. (eds) Big Data Analytics. BDA 2019. Lecture Notes in Computer Science(), vol 11932. Springer, Cham. https://doi.org/10.1007/978-3-030-37188-3_21

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  • DOI: https://doi.org/10.1007/978-3-030-37188-3_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37187-6

  • Online ISBN: 978-3-030-37188-3

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