Abstract: Text stance detection aims to infer users’ opinions on specific topics,such as supportive,opposing,neutral and other attitudes,from their published texts.Traditional stance detection studies often use deep learning models such as convolutional neural networks or long and short-term memory networks to learn the basic semantic information of the text,ignoring the syntactic structure information embedded in the text.To address this problem,this paper designs and implements a text stance detection model－－AT-BiLSTM-GAT based on topic attention and dependent syntax,and on the basis of the text context information extracted by BiLSTM,GAT is used to further learn dependent syntactic information at the text linguistic level.Meanwhile,a topic attention mechanism incorporating contextual semantic information is designed and implemented,and scaled dot product attention is employed to learn the topic-related important content in stance text,and comparative experiments on public datasets prove the efficiency of the designed and implemented AT-BiLSTM-GAT model.Finally,to address the problem of the small size of the stance detection research dataset,a synonym replacement data enhancement scheme based on WordNet synonym database and WebVectors word embedding model－WWDA,which ensures the lexical correctness and semantic similarity of the synonym replacement process,and experiment proves that it can generate more high-quality samples and improve the detection performance of the model.

Key words: Stance detection, Topic attention, Dependency syntax, Graph attention network, Data augmentation