KNOWLEDGE-AWARE PROMPT LEARNING FRAMEWORK FOR KOREAN-CHINESE MICROBLOG SENTIMENT ANALYSIS

The Korean-Chinese language spoken by the Chinese Koreans, a cross-border ethnic group in China, has distinct linguistic characteristics compared to the standard Korean. Despite the increasing presence of Korean-Chinese microblogs on the Sina Microblog Platform, sentiment analysis in this language is still in its early stages. To bridge this gap, we construct a Korean-Chinese Microblog Sentiment Analysis (KCMSA) dataset. To maximize the benefits of Pre-trained Language Models, we propose the Knowledge-Aware Prompt Learning Framework (KAP). Our framework utilizes prompt learning and integrates Korean sentiment knowledge base to enhance accuracy, and leverages multiple refinement operations to reduce the introduced noise. Baseline evaluations and experiments over existing Korean short-text social platform datasets demonstrate the superiority of KAP.