Few-shot text-based emotion detection

Teodor-George Marchitan, Claudiu Creanga, Liviu P. Dinu
7/8/2025
cs.CL

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Abstract

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.

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Cite this paper

@article{marchitan2025fewshot,
  title  = {Few-shot text-based emotion detection},
  author = {Teodor-George Marchitan and Claudiu Creanga and Liviu P. Dinu},
  year   = {2025},
  eprint = {2507.05918},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.05918v1}
}

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