有用性
计算机科学
可读性
数据科学
产品(数学)
一致性(知识库)
社会化媒体
情绪分析
万维网
人工智能
心理学
几何学
数学
社会心理学
程序设计语言
作者
Sunil Saumya,Pradeep Kumar Roy,Jyoti Prakash Singh
标识
DOI:10.1016/j.engappai.2023.107075
摘要
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.
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