@inproceedings{muhammad-etal-2025-brighter,
    title = "{BRIGHTER}: {BRI}dging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages",
    author = "Muhammad, Shamsuddeen Hassan  and
      Ousidhoum, Nedjma  and
      Abdulmumin, Idris  and
      Wahle, Jan Philip  and
      Ruas, Terry  and
      Beloucif, Meriem  and
      de Kock, Christine  and
      Surange, Nirmal  and
      Teodorescu, Daniela  and
      Ahmad, Ibrahim Said  and
      Adelani, David Ifeoluwa  and
      Aji, Alham Fikri  and
      Ali, Felermino D. M. A.  and
      Alimova, Ilseyar  and
      Araujo, Vladimir  and
      Babakov, Nikolay  and
      Baes, Naomi  and
      Bucur, Ana-Maria  and
      Bukula, Andiswa  and
      Cao, Guanqun  and
      Tufi{\~n}o, Rodrigo  and
      Chevi, Rendi  and
      Chukwuneke, Chiamaka Ijeoma  and
      Ciobotaru, Alexandra  and
      Dementieva, Daryna  and
      Gadanya, Murja Sani  and
      Geislinger, Robert  and
      Gipp, Bela  and
      Hourrane, Oumaima  and
      Ignat, Oana  and
      Lawan, Falalu Ibrahim  and
      Mabuya, Rooweither  and
      Mahendra, Rahmad  and
      Marivate, Vukosi  and
      Panchenko, Alexander  and
      Piper, Andrew  and
      Ferreira, Charles Henrique Porto  and
      Protasov, Vitaly  and
      Rutunda, Samuel  and
      Shrivastava, Manish  and
      Udrea, Aura Cristina  and
      Wanzare, Lilian Diana Awuor  and
      Wu, Sophie  and
      Wunderlich, Florian Valentin  and
      Zhafran, Hanif Muhammad  and
      Zhang, Tianhui  and
      Zhou, Yi  and
      Mohammad, Saif M.",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.acl-long.436/",
    doi = "10.18653/v1/2025.acl-long.436",
    pages = "8895--8916",
    ISBN = "979-8-89176-251-0",
    abstract = "People worldwide use language in subtle and complex ways to express emotions. Although emotion recognition{--}an umbrella term for several NLP tasks{--}impacts various applications within NLP and beyond, most work in this area has focused on high-resource languages. This has led to significant disparities in research efforts and proposed solutions, particularly for under-resourced languages, which often lack high-quality annotated datasets.In this paper, we present BRIGHTER{--}a collection of multi-labeled, emotion-annotated datasets in 28 different languages and across several domains. BRIGHTER primarily covers low-resource languages from Africa, Asia, Eastern Europe, and Latin America, with instances labeled by fluent speakers. We highlight the challenges related to the data collection and annotation processes, and then report experimental results for monolingual and crosslingual multi-label emotion identification, as well as emotion intensity recognition. We analyse the variability in performance across languages and text domains, both with and without the use of LLMs, and show that the BRIGHTER datasets represent a meaningful step towards addressing the gap in text-based emotion recognition."
}