Do Emotions Matter? Exploring the Distribution of Emotions in Online Product Reviews

Abstract

Word-of-mouth (WOM) in the form of online customer reviews has received considerable attention by practitioners and academics. Prior literature has focused more on the understanding of the phenomenon using the frequency or overall rating/valence information of WOM such as the causal effect on consumer choice, distribution pattern of WOM, and its product type-dependency, while questions on how firms can potentially use or design online WOM platforms and benefit from it based on the content of WOM are still open, and needs more attention from researchers. In addition, an important antecedent for the generation of word-of-mouth is a strong emotional response, which in turn triggers the consumer to post a customer review online. However, only a limited number of studies to date have actually examined the content of reviews for their emotional content. To fill this gap, we analyzed the emotional content of a large number of online product reviews using Natural Language Processing (NLP) methods. We found that more extreme reviews have a greater proportion of emotional content than less extreme reviews, revealing a bimodal distribution of emotional content, thereby empirically validating a key assumption that underpins much of the extant literature on online WOM. In addition, we found reviews have a greater proportion of positive emotional content within positive extreme ratings as compared to negative emotional content within negative extreme ratings which is a major factor in online WOM generation. Investigating further, we did find that there is a difference in the emotional content of reviews between search and experience goods in the early stages of product launch. However, interestingly, we find that these differences disappear over time as the addition of reviews reduces the information asymmetry gap. This provides important evidence to the widely held notion that on the Internet, all goods become search goods. Our findings suggest important managerial implications regarding product development, advertisement, and platform design using WOM content.