mteb/emotion

Tasks

• text-classification

Citation text from the card

@inproceedings{saravia-etal-2018-carer,
  abstract = {Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.},
  address = {Brussels, Belgium},
  author = {Saravia, Elvis  and
Liu, Hsien-Chi Toby  and
Huang, Yen-Hao  and
Wu, Junlin  and
Chen, Yi-Shin},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  doi = {10.18653/v1/D18-1404},
  editor = {Riloff, Ellen  and
Chiang, David  and
Hockenmaier, Julia  and
Tsujii, Jun{'}ichi},
  month = oct # {-} # nov,
  pages = {3687--3697},
  publisher = {Association for Computational Linguistics},
  title = {{CARER}: Contextualized Affect Representations for Emotion Recognition},
  url = {https://aclanthology.org/D18-1404},
  year = {2018},
}


@article{enevoldsen2025mmtebmassivemultilingualtext,
  title={MMTEB: 

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