This paper proposes a text-analytics framework that integrates aspect-level sentiment analysis (ASLSA) with bias-corrected least square dummy variable (LSDVc) – a panel data regression method – to empirically examine the influence of review-embedded information on product sales. We characterize the online perceptions as consumer opinions or sentiments corresponding to the product features discussed within the review. While ASLSA discovers key product features and quantifies the opinions in corresponding content, the LSDVc-based panel data regression analyses the consumer sentiments to explore their influence on product sales. The proposed framework is tested on the mid-sized car segment in India. Our findings suggest that review volume and the sentiments corresponding to the exterior and appearance significantly influence the mid-size car sales in India.

本文提出了一种文本分析框架，该框架将方面级情感分析 (ASLSA) 与偏差校正最小二乘虚拟变量 (LSDVc)（一种面板数据回归方法）相结合，以实证检验嵌入评论的信息对产品销售的影响。我们将在线感知描述为与评论中讨论的产品功能相对应的消费者意见或情绪。在 ASLSA 发现关键产品功能并量化相应内容中的意见时，LSDVc-基于面板数据回归分析消费者情绪以探索其对产品销售的影响。提议的框架在印度的中型汽车市场上进行了测试。我们的研究结果表明，评论量以及与外观和外观相对应的情绪显着影响了印度的中型汽车销量。