## 1 Introduction  
Recommendation systems are essential in addressing the problem of information overload. However, traditional models are susceptible to exposure and selection biases [1], which can lead to suboptimal outcomes. This is due to the limited number of items that users can evaluate and their incomplete understanding of their own preferences. In the context of recommendation systems, selection bias refers to the tendency of users to choose items that do not accurately reflect their true interests or preferences. Importantly, user recommendations may not always be objective and may involve subjective evaluations. Users may exhibit bias toward certain items for various reasons, further influencing the recommendations generated by the system. Consequently, these recommendations may not align with the user's actual preferences [2, 3, 4, 5]. The presence of selection bias in recommendation systems can lead to several problematic outcomes, negatively affecting their performance. This can result in reduced accuracy and overspecialization of recommendations [6], as they may reflect users' selection preferences rather than their actual interests. To mitigate selection bias in recommendation systems, we focus on investigating the following three factors influencing selection bias: data sparsity [7], selectivity of user behavior [8], and high demands of IPS methods [9]. First, interaction data in recommendation systems are typically highly sparse because users can only access and interact with a limited number of items. Second, during the process of rating or reviewing a small subset of items, users selectively provide feedback based on their preferences, leading to data imbalance. Due to data sparsity and imbalance, recommendation systems often focus on explicit user ratings while ignoring implicit factors in rating and review data, such as personal sentimental fluctuations, subjective preference differences, and social environmental influences. This directly contributes to the formation of sentiment bias. At present, the utilization of sentiment analysis to remove sentiment bias and alleviate data sparsity represents a promising approach that is unlikely to give rise to modeling difficulties owing to differences in data structures. A subset of researchers categorize reviews into several categories through sentiment analysis (a common method is binary categorization into positive and negative reviews) [10; 11]. By analyzing user preferences in this way, more accurate recommendations can be made [12; 13]. It is important to ensure that user ratings and reviews of products are obtained simultaneously, as this enables more accurate recommendations to be made on the basis of comprehensive and authentic opinions. However, the current study found the sentiment bias contained in the text, but it is basically in the direction of text processing [14; 15], and does not consider that the selection bias in recommendation systems is also affected by the sentiment bias due to data sparsity. Existing studies primarily adopt binary classification methods, which can only coarsely identify positive or negative sentiments and generate labels. These methods fail to align with users' 0-to-5 rating scales, resulting in poor performance in correcting sentiment bias between ratings and reviews. To address this, we construct a sentiment analysis module that employs dual attention vectors to analyze user reviews and derive fine-grained sentiment scores (0-5) instead of binary labels. This approach more precisely represents the nuanced emotions in user reviews and visually captures sentiment bias between ratings and reviews. Additionally, we design a refined composite function to resolve sentiment bias and alleviate data sparsity. We then analyze the IPS-based debiasing method. Currently, IPS-based methods have become the cornerstone for selection debiasing. [PERSON] et al. [5] introduced the fundamental principle of IPS: reweighting training samples to adjust data distributions and mitigate selection bias.