Comprehensiveness is one of the most important textual content features of online review and exhibits significant impacts on consumer’s buying decisions. This paper explores the effect of air quality on review comprehensiveness by using a large-scale daily restaurant review dataset. By applying panel data and text mining method, we report an emotion-based underlying mechanism for the link between ambient air pollution levels and review comprehensiveness. Specifically, we show that air pollution levels significantly decrease review comprehensiveness, and emotion arousal mediates the relationship. Besides, our analyses reveal that the effect of the changing natural environment on reviews is asymmetrical, such that the negative relationship between air pollution levels and emotional arousal is stronger among novice reviewers. This research extends our current understanding of air pollution’s psychological and behavioral effects on review writers and suggests the importance of integrating air quality information into online review management strategies.

全面性是在线评论最重要的文字内容特征之一，对消费者的购买决策产生重大影响。本文通过使用大型日常餐厅评论数据集，探索空气质量对评论综合性的影响。通过应用面板数据和文本挖掘方法，我们报告了一种基于情绪的潜在机制，用于环境空气污染水平与评估全面性之间的联系。具体而言，我们表明，空气污染水平显着降低了评论的综合性，而情绪唤醒则介导了这种关系。此外，我们的分析表明，自然环境的变化对评论的影响是不对称的，因此，新手审稿人中空气污染水平与情绪唤醒之间的负相关性更强。