From Valence to Emotions : Exploring the Distribution of Emotions in Online Product Reviews

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, 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 imbalance known as schema discrepancy, which is considered to trigger the consumer to post a customer review online. However, only a limited number of studies to date have actually examined the emotional content of reviews to validate this line of reasoning. To fill this gap, we analyzed the emotional content of a large number of online product reviews using Natural Language Processing (NLP) methods. We 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, these differences disappear over time as the addition of reviews reduces the information asymmetry gap. This suggests that traditional experience goods are evaluated more like search goods in online environments, because consumers can easily evaluate attributes of products prior to purchase based on the reviews accumulated. In addition, we find 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. Furthermore, 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, and helps explain the commonly observed J-shaped distribution of reviews. Our findings suggest important managerial implications regarding product development, advertisement, and platform design using WOM content