Interpretable Perceived Topics in Online Customer Reviews for Product Satisfaction and Reader Helpfulness

Online customer reviews contain useful and important information, particularly, for product development and management, because customers praise or criticize in their reviews certain product attributes. We propose a model that extracts perceived topics from textual reviews using natural language processing under some restrictions created using seed words for improving the topic interpretability. In addition, the proposed model estimates the relationships between the topics and product satisfaction by writers of the review and the perceived helpfulness of reviews by readers, that is, these textual reviews are viewed as current product evaluations by customers who have made purchases and expectations of possible future demand by consumers who have yet to make purchases. The empirical study on e-commerce food reviews shows that our proposed model performs better than the extant alternative models and provides interesting findings such that the "ingredient" topic in the review text decreases the levels of customer satisfaction and the reader's perceived helpfulness. In contrast, the "health" topic increases the levels of both customer satisfaction and the reader's perceived helpfulness. These findings help us understand the product attributes that purchased customers are satisfied with and for which readers of reviews find helpful information.