当前位置: X-MOL 学术Artif. Intell. Rev. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Exploring the influential reviewer, review and product determinants for review helpfulness
Artificial Intelligence Review ( IF 12.0 ) Pub Date : 2018-10-13 , DOI: 10.1007/s10462-018-9662-y
M. S. I. Malik , Ayyaz Hussain

Helpfulness of online reviews is a multi-faceted concept. The reviews are usually ranked on the basis of perceived helpful votes and aid in making purchase decisions for online customers. This study extends the prior work done for review helpfulness by considering not only the influential characteristics of reviews but also incorporates influential indicators of reviewer and product category. Influential factor based new features (product, reviewer and review) are proposed to predict the helpfulness of online reviews by using five ML methods. The experimental analysis on a real-life review dataset shows that the hybrid set of proposed features deliver the best predictive performance. In addition, the reviewer and the review category features introduced in this research exhibit better predictive performance as a standalone model. Findings show that reviews which have large number of comments, large values of sentiment and polarity scores receive more helpful votes. The reviewer activity length and recency are statistically significant predictors for helpfulness prediction. In addition, number of question answered, ratio of positive reviews and average rating per review are also significant variables of product type. The findings of this study highlight the number of implications for research and provide new insights to retailers for efficient ranking and organization of consumer reviews for online users.

中文翻译:

探索有影响力的评论者、评论和产品决定因素以提高评论有用性

在线评论的有用性是一个多方面的概念。评论通常根据感知到的有用投票进行排名,并有助于为在线客户做出购买决定。本研究不仅考虑了评论的影响特征,还考虑了评论者和产品类别的影响指标,从而扩展了之前为评论有用性所做的工作。提出了基于影响因素的新特征(产品、评论者和评论),通过使用五种 ML 方法来预测在线评论的有用性。对真实评论数据集的实验分析表明,提出的特征的混合集提供了最佳的预测性能。此外,本研究中引入的评论者和评论类别特征作为独立模型表现出更好的预测性能。调查结果表明,评论数量多、情绪值和极性分数大的评论会获得更多有用的选票。评论者活动长度和新近度是有用性预测的统计显着预测因子。此外,回答的问题数量、好评率和每条评论的平均评分也是产品类型的重要变量。这项研究的结果突出了研究的意义,并为零售商提供了新的见解,以有效地对在线用户的消费者评论进行排名和组织。正面评论的比率和每次评论的平均评分也是产品类型的重要变量。这项研究的结果突出了研究的意义,并为零售商提供了新的见解,以有效地对在线用户的消费者评论进行排名和组织。正面评论的比率和每次评论的平均评分也是产品类型的重要变量。这项研究的结果突出了研究的意义,并为零售商提供了新的见解,以有效地对在线用户的消费者评论进行排名和组织。
更新日期:2018-10-13
down
wechat
bug