This work examines the question of who is more likely to provide future helpful reviews in the context of online product reviews by synergistically using personality theories and data analytics. It trains a deep learning model to infer a reviewer’s personality traits. This enables analyses to reveal the role of personality traits in review helpfulness among a large population of reviewers. We develop hypotheses on how personality traits are associated with review helpfulness, followed by hypotheses testing that confirms that higher review helpfulness is related to higher openness, conscientiousness, extraversion, and agreeableness and to lower emotional stability. These results suggest the appropriateness of using these five personality traits as inputs for developing a model for predicting future review helpfulness. Based on an ensemble model using supervised classification algorithms, we develop a predictive model and demonstrate its superior performance. Theoretical and practical implications are discussed.

中文翻译：

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评估不认识的人：推断的审稿人个性和审稿有用性

这项工作通过协同使用个性理论和数据分析来检验谁更有可能在在线产品评论的背景下提供未来有用的评论。它训练一个深度学习模型来推断审稿人的性格特征。这使得分析能够揭示人格特征在大量评论者的评论帮助中的作用。我们提出关于人格特征如何与评论帮助相关的假设，然后进行假设检验，确认较高的评论帮助与较高的开放性、责任心、外向性和随和性以及较低的情绪稳定性有关。这些结果表明，使用这五个人格特征作为输入来开发预测未来评论有用性的模型是合适的。基于使用监督分类算法的集成模型，我们开发了一个预测模型并展示了其卓越的性能。讨论了理论和实践意义。