# A Multi-Stance Detection Method by Fusing Sentiment Features
## 1 Introduction  
News hotspots can be easily discussed thanks to platforms such as Weibo and Twitter, which has resulted in a substantial increase in the amount of user-generated content. These messages represent users' opinions and stances and have significant influence on market strategy, government policy, and public opinion. The task of stance detection in Chinese microblogs was proposed in NLPCC-ICCPOL (2016), indicating that Chinese stance information is of tremendous research significance. Stance information is an important basis in fields such as market research, public opinion management, and policy formulation. In market research, stance detection helps companies understand public perceptions of their products. In the field of public opinion management, stance information is crucial for early warning and response strategies regarding public sentiment. Furthermore, when formulating policies, government agencies can utilize stance information to understand public opinions. Additionally, as one of the most widely used languages globally, Chinese stance detection presents unique challenges and opportunities for Natural Language Processing (NLP).  
The current focus of stance detection is on improving stance detection accuracy by employing various models and methods. These approaches usually divide stances into three categories: support, opposition, and neutral. However, when expressing opinions, users not only differ in polarity but also in the degree of their opinions. Polarity typically refers to the positive or negative tendency of a stance, but the range of opinions extends beyond simple binary classification and includes a gradient of sentiments ranging from mild agreement to strong criticism. For example, when expressing support, some users may agree totally while others may agree with only one component. The same is true when users offer an opposing viewpoint. This reflects the complexity of human sentiments and the diversity of expressing viewpoints. In such circumstances, the traditional stance category is too simplistic to depict the diversity of users' stances. The degree of strength in expressing an opinion can be seen as the will or belief behind the opinion. An individual may express their viewpoint with a casual tone, or they may defend their stance with firm conviction. These varying degrees significantly influence people's perspectives. In the field of opinionmining, understanding the importance of this degree is just as crucial as understanding polarity. Grasping these subtle differences is essential for constructing more sophisticated sentiment analysis and stance detection models, enabling a more accurate capture of comprehensive public opinions.  
Traditional approaches, on the other hand, usually consider only the text itself, ignoring the sentimental features buried in it. Sentiment features have a supportive effect on stance detection, and when stance detection is combined with sentiment analysis, it is possible to filter away the influence of moody expressions. Therefore, there are limitations to stance detection, and a more comprehensive picture of user attitudes can be acquired by combining sentiment analysis.  
This paper aims to address the following issues: first, to classify stances into five categories based on the degree and polarity of users' attitudes to describe users' stances more thoroughly; second, to mine the reasons for users' different attitudes through the five categories of stances, which can be used to guide public opinion management; and third, to combine sentiment analysis with stance detection to gain a more comprehensive understanding of users' opinions. As a result, this paper introduces BACF (bi-LSTM-Attention-CNN-Fusion), a multi-stance detection model that combines deep learning with the sentiment lexicon to address the above issue. The experiment shows that BACF conducts stance detection more accurately than the traditional approach, which is significant for policy selection and public opinion analysis.