Biases and de-biasing in recommender systems have received increasing attention recently. This study focuses on a newly identified bias, i.e., sentiment bias, which is defined as the divergence in recommendation performance between positive users/items and negative users/items. Existing methods typically employ a regularization strategy to eliminate the bias. However, blindly fitting the data without modifying the training procedure would result in a biased model, sacrificing recommendation performance.