Review helpfulness prediction on e-commerce websites: A comprehensive survey

Abstract

This comprehensive survey investigates methodologies and factors utilized for predicting review helpfulness on e-commerce websites. Analyzing 132 research publications from the past 17 years, four primary determinants come to light: textual contents, non-textual contents, reviewer-related factors, and product-related factors. Review length, readability, entropy, sentiments, review rating, product description features, and customer question-answer features emerge as influential indicators. The study revealed a shift from statistical processes to machine learning and neural learning approaches in recent years due to their superior performance in predicting review helpfulness. The survey findings open up promising avenues for future research. Key directions include addressing the challenges posed by duplicate reviews, ensuring review-rating consistency, and leveraging helpful reviews in the development of chatbot systems for e-commerce websites. Additionally, exploring the impact of social media sentiment on product recommendations presents intriguing possibilities. This survey provides valuable insights for researchers and practitioners in the realm of review helpfulness prediction on e-commerce websites.