The Secret of Metaphor on Expressing Stronger Emotion
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Abstract (arXiv)
Metaphors are proven to have stronger emotional impact than literal expressions. Although this conclusion is shown to be promising in benefiting various NLP applications, the reasons behind this phenomenon are not well studied. This paper conducts the first study in exploring how metaphors convey stronger emotion than their literal counterparts. We find that metaphors are generally more specific than literal expressions. The more specific property of metaphor can be one of the reasons for metaphors' superiority in emotion expression. When we compare metaphors with literal expressions with the same specificity level, the gap of emotion expressing ability between both reduces significantly. In addition, we observe specificity is crucial in literal language as well, as literal language can express stronger emotion by making it more specific.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 1 of its 8 references that are in the map. First posted on arXiv 30 Jan 2023.
Builds on
1 of 8 references.
References · 1
- 2022Nominal Metaphor Generation with Multitask Learningcited by 8
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Citing papers per year: 2023: 4; 2024: 4; 2025: 1.
9 papers.
- 2023FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learningcited by 18
- 2023Metaphor Detection with Effective Context Denoisingcited by 13
- 2023Metaphor Detection via Explicit Basic Meanings Modellingcited by 12
- 2024MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Languagecited by 10
- 2025EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese Metaphorscited by 4
- 2024Finding Challenging Metaphors that Confuse Pretrained Language Modelscited by 3
- 2024Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrasescited by 3
- 2024Overview of the NLPCC 2024 Shared Task on Chinese Metaphor Generationcited by 2
- 2023Deep Learning-Based Knowledge Injection for Metaphor Detection: A Comprehensive Reviewcited by 0
- 2025EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese Metaphors12 May 2025
- 2024Overview of the NLPCC 2024 Shared Task on Chinese Metaphor Generation8 Aug 2024
- 2024MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language19 Jun 2024
- 2024Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases20 Mar 2024
- 2024Finding Challenging Metaphors that Confuse Pretrained Language Models29 Jan 2024
- 2023Deep Learning-Based Knowledge Injection for Metaphor Detection: A Comprehensive Review8 Aug 2023
- 2023Metaphor Detection via Explicit Basic Meanings Modelling26 May 2023
- 2023Metaphor Detection with Effective Context Denoising11 Feb 2023
- 2023FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning9 Feb 2023