# A Multi-Stance Detection Method by Fusing Sentiment Features
## 2 Related Research
### Stance Detection Models  
Traditional stance detection frequently employs support vector machines [8], naive Bayes, logistic regression, random forests [9], K-means, and other machine learning models [10]. Machine learning models are well defined and simple in structure. For instance, a two-stage stance detection system based on SVM was proposed to characterize stances on Twitter [11]. [PERSON] et al. discovered that random forests, linear SVM, and Gaussian NB may be employed as majority vote stance identification classifiers [12]. However, machine learning models are linguistically demanding and prone to human error. As the volume of information increases, traditional stance detection models become more time-consuming and expensive, and mining stance information is becoming increasingly difficult in the information age.  
Deep learning models, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), have become the standard methodologies for stance detection. RNNs are good at processing sequential data. In Twitter stance detection, [PERSON] proposed a variant that utilizes bi-LSTMs and nested LSTMs to capture long-term dependencies, where each module is enhanced with an attention mechanism [13]. In the two-target stance detection task, [PERSON] et al. used multiple LSTM layers to encode target-related regions [14]. CNNs are well suited for extracting local features because of their multiple kernels. [PERSON] et al. proposed a CNN-based stance analysis approach to identify stance tendencies [15]. [PERSON] et al. employed BERT and CNNs to create a stance detection model for Vietnamese [16]. With the advancement of deep learning, researchers have begun to investigate hybrid model structures. [PERSON] et al. produced an excellent result using a hybrid stance detection model built with GRU, position weight vector, and CNNs [17]. In the field of rumor stance detection, [PERSON] et al. proposed a framework based on multi-graph neural networks to capture the attribute and structural information of context [18]. In the task of stance detection on social platforms, [PERSON] et al. employed a Gated Graph Neural Network (GGNN) approach to integrate structural information between reviews [19]. Furthermore, technologies such as sentiment lexicons and attention mechanisms have been used to improve the model's performance. The sentiment lexicon can be used to obtain sentiment information from a text. For example, [PERSON] et al. employed the sentiment lexicon to perform feature selection in microblog stance detection and found favorable results [20]. Although sentiment lexicons are commonly utilized as a supplement, the combination of sentiment analysis and stance detection is still in its early stages. Moreover, the attention process may give more weight to significant information, which can improve model accuracy. [PERSON] et al. proposed a two-stage model based on LSTM in combination with an attention mechanism that can perform well in Twitter stance detection [21]. Additionally, [PERSON] et al. implemented word embeddings for stance detection models using BERT [22]. In conclusion, deep learning has surpassed machine learning as the dominant model for stance detection, and the attention mechanism and sentiment lexicon have been employed to improve model efficacy. However, while binary classification has given way to three-class classification, the question of how to further extend stance classification remains unanswered. In addition, the sentiment information contained in stance expressions has not been fully utilized. To address these issues, this paper proposes a multi-stance detection model by fusing sentiment features.