Computational Approach to Prediction of Attitude Change Through eWOM Messages Involving Subjective Rank Expressions

摘要

—Electronic word-of-mouth (eWOM) is an important information source that influences consumer product evaluations. This paper presents a computational model that predicts the potency-magnitude relations of eWOM messages involving subjective rank expressions, which refer to the linguistic representations related to the attitude-levels of the benefits of the product attributes. The amount of required inference for the message receiver to know the attitude-level through the message is quantified as inference quantum by using inference space, which is characterized by two evaluation parameters: evaluation target size and evaluation scale size. The computational model incorporates the idea of inference quantum into the cognitive hypotheses that were developed to account for the potency differences with reference to the expertise levels - experts or novices - of the message receiver of the products. By applying the computational model to simple eWOM messages, the potency-magnitude relations were observed to depend critically on the values of the message receiver’s evaluation parameters. This paper defines three messageclasses, which are also studied in the areas of opinion mining and sentiment analysis, and investigates mathematically how the potency-magnitude relations change based on the values of the evaluation parameters.