A Computational Account of Potency Differences in eWOM Messages Involving Subjective Rank Expressions

Electronic word-of-mouth (eWOM) is one important information source that influences consumer product evaluations. This paper presents a computational model that accounts for the potency differences in eWOM messages involving subjective rank expressions, which refer to linguistic representations related to the evaluated levels of the benefits of product attributes. The computational model postulates various types of subjective rank expressions obtained using techniques from opinion mining and sentiment analysis. The computational model incorporates the idea of inference space that contains all possible evaluated levels inferred by the message receiver. The computational model (1) evaluates inference quantum of each message numerically by using the inference space and (2) derives potency-magnitude relations by discerning the expertise level of the message receiver of the products. The unexplored behavioral hypotheses suggested by the computational model are discussed from an analytical viewpoint.