Face of Cross-Dissimilarity: Role of Competitors’ Online Reviews Based on Semi-Supervised Textual Polarity Analysis

Existing online review research has not fully captured consumer purchasing behavior in complex decision-making environments, particularly in contexts involving multiple product comparisons and conflicting review perspectives. This study thoroughly investigates the impact on focal product purchase decisions when consumers compare multiple products and face information inconsistency. Based on online review data from JD.com, we propose a semi-supervised deep learning model to analyze consumers’ sentiment polarity toward product attributes. The method establishes implicit relationships between labeled and unlabeled data through consistency regularization. Subsequently, we conceptualize three types of online review dissimilarity factors, rating-sentiment dissimilarity, cross-review dissimilarity, and brand dissimilarity, and employ regression models to examine the impact of competing products’ online reviews on focal product sales. The results indicate that by employing a semi-supervised deep learning approach, unlabeled data are annotated with pseudo-labels and utilized for model training, achieving more accurate sentiment classification than using labeled data alone. Moreover, positive (negative) sentiment attributes of competing products have a significant negative (positive) effect on focal product purchases. Online review dissimilarity moderates the spillover effects of competing products. Notably, these spillover effects are more pronounced when competing products are from the same brand compared to different brands. The research findings not only highlight the heterogeneous effects of positive and negative sentiments but also provide a new perspective for examining dissimilarity, enriching the understanding of online review spillover effects and the role of dissimilarity, while offering practical guidance for resource allocation decisions by companies and platforms.