# A Multi-Stance Detection Method by Fusing Sentiment Features
## 2 Related Research
### Stance Category  
In the beginning, stance detection was a problem of binary classification, with stances categorized as \"support\" or \"oppose\". As stances were better understood, stance detection shifted from a binary to a three-class classification, and researchers began categorizing stances as \"support, oppose, or neither\" [1]. For instance, [PERSON] et al. categorized stances as \"support, oppose, and neutral\" in their study on the stance recognition of users' viewpoints [2]. As can be seen, stance classification is a significant advancement in the field of stance detection. The simple three-class category can no longer effectively reflect user opinions, and some scholars have empirically proposed a four-class category. However, stance classification based on experience is limited in generalization and focuses on specific fields. [PERSON] et al. categorized the stances of the two datasets used for rumor detection as \"support, deny, question, and comment\" and \"agree, disagree, discuss, and unrelated\" [3]. For the purpose of stance detection in Twitter rumors, [PERSON] et al. classified the stance into four categories: \"remark, support, deny, and query\" [4]. How to extend the stance category is a significant problem for stance detection.  
Stance grading can assist in resolving the problem. Users' stances differ greatly in terms of polarity and degree, which allows for the possibility of stance grading. When users comment on various topics, this phenomenon becomes more obvious. For example, when a new policy is proposed, distinct discussions may focus on its current impact and potential future benefits. People will have different preferences, as with their levels of support and opposition. As a result, users with various stances may focus on different topics. It is feasible to acquire a more precise understanding of users' attitudes by grading their stances according to various topics. Extending segmented stances in terms of grading is a standard practice that can better represent user attitudes. For instance, five-level scales are frequently created in the marketing industry based on user satisfaction.  
To achieve degree grading in the field of stance detection, we can first mine the event's topics and group them into various stances based on their degree and polarity to create a multi-stance indicator system. The LDA topic model (Latent Dirichlet Allocation) is an unsupervised learning technique for text analysis and topic mining that was first proposed by [PERSON] et al. [5]. LDA acquires text topics through the statistical analysis and probabilistic modeling of words in the text. In order to analyze the experiences of Airbnb guests during the COVID-19 crisis, [PERSON] examined the reviews posted on the Airbnb platform using the LDA [6]. To present a complete picture of the research field, [PERSON] employed the LDA to infer the research topics about anthropogenic marine debris [7]. As a result, the LDA model can effectively implement hidden topic mining. It can better reflect users' attitudes by mining text topics and building a multi-stance indicator system with LDA models.