Large language models prompt engineering as a method for embodied cognitive linguistic representation: a case study of political metaphors in Trump’s discourse
Embodied-Cognitive Linguistics inherits and further develops the core concepts of Cognitive Linguistics, maintaining a focus on embodied cognition and conceptual metaphors. It emphasizes that language is not merely a cognitive phenomenon but also a product of human social interactions and economic c...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1591408/full |
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| author | Haohan Meng Xiaoyu Li Jinhua Sun |
| author_facet | Haohan Meng Xiaoyu Li Jinhua Sun |
| author_sort | Haohan Meng |
| collection | DOAJ |
| description | Embodied-Cognitive Linguistics inherits and further develops the core concepts of Cognitive Linguistics, maintaining a focus on embodied cognition and conceptual metaphors. It emphasizes that language is not merely a cognitive phenomenon but also a product of human social interactions and economic conditions. From this perspective, metaphors extend beyond their simple linguistic representation and become essential structures of human cognitive expression. Political metaphors, in particular, are instrumental in shaping public ideology and emotional engagement, a phenomenon clearly demonstrated in the political speeches of Donald Trump. With 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 ChatGPT-4.0 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. 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. |
| format | Article |
| id | doaj-art-3a563b64676c48409834b47c955b4a2c |
| institution | Matheson Library |
| issn | 1664-1078 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj-art-3a563b64676c48409834b47c955b4a2c2025-06-25T15:23:52ZengFrontiers Media S.A.Frontiers in Psychology1664-10782025-06-011610.3389/fpsyg.2025.15914081591408Large language models prompt engineering as a method for embodied cognitive linguistic representation: a case study of political metaphors in Trump’s discourseHaohan MengXiaoyu LiJinhua SunEmbodied-Cognitive Linguistics inherits and further develops the core concepts of Cognitive Linguistics, maintaining a focus on embodied cognition and conceptual metaphors. It emphasizes that language is not merely a cognitive phenomenon but also a product of human social interactions and economic conditions. From this perspective, metaphors extend beyond their simple linguistic representation and become essential structures of human cognitive expression. Political metaphors, in particular, are instrumental in shaping public ideology and emotional engagement, a phenomenon clearly demonstrated in the political speeches of Donald Trump. With 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 ChatGPT-4.0 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. 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.https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1591408/fullcognitive linguisticslarge language modelsmetaphor identificationpolitical discourseembodied cognitionamerican studies |
| spellingShingle | Haohan Meng Xiaoyu Li Jinhua Sun Large language models prompt engineering as a method for embodied cognitive linguistic representation: a case study of political metaphors in Trump’s discourse Frontiers in Psychology cognitive linguistics large language models metaphor identification political discourse embodied cognition american studies |
| title | Large language models prompt engineering as a method for embodied cognitive linguistic representation: a case study of political metaphors in Trump’s discourse |
| title_full | Large language models prompt engineering as a method for embodied cognitive linguistic representation: a case study of political metaphors in Trump’s discourse |
| title_fullStr | Large language models prompt engineering as a method for embodied cognitive linguistic representation: a case study of political metaphors in Trump’s discourse |
| title_full_unstemmed | Large language models prompt engineering as a method for embodied cognitive linguistic representation: a case study of political metaphors in Trump’s discourse |
| title_short | Large language models prompt engineering as a method for embodied cognitive linguistic representation: a case study of political metaphors in Trump’s discourse |
| title_sort | large language models prompt engineering as a method for embodied cognitive linguistic representation a case study of political metaphors in trump s discourse |
| topic | cognitive linguistics large language models metaphor identification political discourse embodied cognition american studies |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1591408/full |
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