Political Metaphor Analysis Based on Large Language Models Prompt Engineering: A Case Study of Trump's Political Speech

Political metaphors are instrumental in shaping public ideology and emotional engagement, a phenomenon clearly demonstrated in the political speeches of Donald Trump. With the rapid advancements in large language models (LLMs) technology, traditional approaches to metaphor identification are undergoing significant transformation. By leveraging the advanced text parsing and generation capabilities of LLMs, new opportunities emerge for the automatic detection and nuanced analysis of political metaphors. This study employs a critical metaphor analysis (CMA) framework, integrated with a chain-of-thought-based prompt engineering (PE) technique, utilizing the ChatGPT4.0 Python environment to identify and examine metaphors in Trump's speeches. The results reveal that Trump strategically employs metaphors derived from diverse source domains-such as Movement and Direction, Illness and Health and Force-to resonate emotionally with his audience. These metaphors serve to reinforce central political themes, including national prospects, political authority, and social change, thereby enhancing the persuasive efficacy and political impact of his rhetoric. Methodologically, while LLMs demonstrate considerable strengths in analyzing political discourse, challenges remain in areas such as semantic differentiation and expression. Future research will focus on optimizing these models, conducting comparative analyses with traditional methods, and exploring their applicability in cross-cultural contexts, with the goal of providing more precise and effective tools for both natural language processing (NLP) and political linguistics research.