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uitnlp/vigoemotions

415 downloads in 30 days3 likesLast modified 31 Mar 2026View on Hugging Face

Permissive or share-alike

The declared licence (mit) is classified as permissive.

Declared licence: mit, from the tagEvidence: README.md at 30f14fc
Licence files: No licence file at the top level of the repositoryEvidence: file list at 30f14fc
Declared sources: None listed in the card metadata
Access gated on Hugging Face: Yes

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Tasks

text-classification

Citation text from the card

@inproceedings{hung-etal-2026-vigoemotions,
    title = "{V}i{G}o{E}motions: A Benchmark Dataset For Fine-grained Emotion Detection on {V}ietnamese Texts",
    author = "Hung, Tran Quang  and
      Nam, Pham Tien  and
      Luu, Son T.  and
      Nguyen, Kiet Van",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.eacl-long.129/",
    doi = "10.18653/v1/2026.eacl-long.129",
    pages = "2805--2831",
    ISBN = "979-8-89176-380-7",
    abstract = "Emotion classification plays a significant role in emotion prediction and harmful content detection. Recent advancements in NLP, particularly through large language models (LLMs), have greatly improved outcomes in this field. This study introduces ViGoEmotions - a Vietnamese emotion corpus comprising 20,664 social media comments in which each comment is classified into 27 fine-grained distinct emotions. To evaluate the quality of the dataset and its impact on emotion classification, eight pre-trained Transformer-based models were evaluated under three preprocessing strategies: preserving original emojis with rule-based normalization, converting emojis into textual descriptions

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dataset_licence({ "id": "uitnlp/vigoemotions" })
signal: "permissive"
stated_licence: "mit"
sources: 0