Research on Weibo Stance Detection Based on Prompt Tuning
Given the challenges faced by traditional machine learning and natural language processing techniques in handling short text data from social media platforms such as Weibo,particularly the impact of data sparsity on data representation and classification performance,this study conducts Weibo stance detection tasks using the prompt tuning method.The prompt tuning approach can effectively explore and utilize the rich knowledge embedded in pre-trained language models to accurately capture and identify the stance tendencies of different text contents towards specific topics.Initially,the study performs data aug-mentation on the Weibo stance detection data based on back-translation,increasing the training data from 3 000 to 12 000 instances.Subsequently,prompt words are designed based on the Weibo text content and corresponding topics.These prompts are intended to guide the attention mechanism of pre-trained lan-guage models to focus on crucial information segments related to stance detection in the text,thereby en-hancing the model's ability to recognize stances in Weibo texts.To validate the effectiveness of prompt fine-tuning in Weibo stance detection tasks,experiments are conducted on the Chinese Weibo stance dataset from NLPCC 2016.The results demonstrate improvements in various key performance indicators compared to existing baseline stance detection methods.Compared to the best baseline method,the Weibo stance detection method based on prompt tuning improved by 0.6％to 6％across five evaluation metrics.In summary,this research not only reveals the significant potential applications of the prompt tuning method in Weibo stance detection tasks but also provides valuable references for future studie.
natural language processingstance detectionprompt tuningGLMdata augmentation
NETL
NSTL
万方数据
