Few-shot text-based emotion detection
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916832476135424 |
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| author | Marchitan, Teodor-George Creanga, Claudiu Dinu, Liviu P. |
| author_facet | Marchitan, Teodor-George Creanga, Claudiu Dinu, Liviu P. |
| contents | This paper describes the approach of the Unibuc - NLP team in tackling the SemEval 2025 Workshop, Task 11: Bridging the Gap in Text-Based Emotion Detection. We mainly focused on experiments using large language models (Gemini, Qwen, DeepSeek) with either few-shot prompting or fine-tuning. With our final system, for the multi-label emotion detection track (track A), we got an F1-macro of $0.7546$ (26/96 teams) for the English subset, $0.1727$ (35/36 teams) for the Portuguese (Mozambican) subset and $0.325$ (\textbf{1}/31 teams) for the Emakhuwa subset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05918 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Few-shot text-based emotion detection Marchitan, Teodor-George Creanga, Claudiu Dinu, Liviu P. Computation and Language This paper describes the approach of the Unibuc - NLP team in tackling the SemEval 2025 Workshop, Task 11: Bridging the Gap in Text-Based Emotion Detection. We mainly focused on experiments using large language models (Gemini, Qwen, DeepSeek) with either few-shot prompting or fine-tuning. With our final system, for the multi-label emotion detection track (track A), we got an F1-macro of $0.7546$ (26/96 teams) for the English subset, $0.1727$ (35/36 teams) for the Portuguese (Mozambican) subset and $0.325$ (\textbf{1}/31 teams) for the Emakhuwa subset. |
| title | Few-shot text-based emotion detection |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.05918 |