Abstract: Zero-shot stance detection aims to identify the author’s attitude towards a specific target or topic when labeled data is limited or nonexistent.Currently,zero-shot stance detection methods are mainly based on attention mechanisms or the incorporation of external sentiment information.However,these methods often neglect the latent sentiment information within the original text and the semantic relationships between entities.To address this issue,a zero-shot stance detection model integrating a sentiment lexicon and graph contrastive learning(EL-CL) is proposed.It employs a chain of thought prompting method to uncover sentiment information within the original text,aiding in the construction of new input texts.During the clustering of input texts to generate prototype graphs,a sentiment lexicon is introduced to enhance the sentiment information within the text vectors of the prototype graph.Additionally,a self-supervised graph contrastive learning method is employed to augment the vectors containing sentiment features,the model’s ability to infer on unseen samples is improved.Experimental results on the public dataset NLPCC2016 Chinese Weibo stance detection demonstrate that,based on five targets,the proposed model improves the macro-F₁ score by 10% over baseline models,which proves its effectiveness in zero-shot stance detection scenarios.

Key words: Zero-shot stance detection, Emotion dictionary, Chain-of-thought, Prototype, Contrastive learning