@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."
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<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.</abstract>
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%0 Conference Proceedings
%T BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages
%A Muhammad, Shamsuddeen Hassan
%A Ousidhoum, Nedjma
%A Abdulmumin, Idris
%A Wahle, Jan Philip
%A Ruas, Terry
%A Beloucif, Meriem
%A de Kock, Christine
%A Surange, Nirmal
%A Teodorescu, Daniela
%A Ahmad, Ibrahim Said
%A Adelani, David Ifeoluwa
%A Aji, Alham Fikri
%A Ali, Felermino D. M. A.
%A Alimova, Ilseyar
%A Araujo, Vladimir
%A Babakov, Nikolay
%A Baes, Naomi
%A Bucur, Ana-Maria
%A Bukula, Andiswa
%A Cao, Guanqun
%A Tufiño, Rodrigo
%A Chevi, Rendi
%A Chukwuneke, Chiamaka Ijeoma
%A Ciobotaru, Alexandra
%A Dementieva, Daryna
%A Gadanya, Murja Sani
%A Geislinger, Robert
%A Gipp, Bela
%A Hourrane, Oumaima
%A Ignat, Oana
%A Lawan, Falalu Ibrahim
%A Mabuya, Rooweither
%A Mahendra, Rahmad
%A Marivate, Vukosi
%A Panchenko, Alexander
%A Piper, Andrew
%A Ferreira, Charles Henrique Porto
%A Protasov, Vitaly
%A Rutunda, Samuel
%A Shrivastava, Manish
%A Udrea, Aura Cristina
%A Wanzare, Lilian Diana Awuor
%A Wu, Sophie
%A Wunderlich, Florian Valentin
%A Zhafran, Hanif Muhammad
%A Zhang, Tianhui
%A Zhou, Yi
%A Mohammad, Saif M.
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F muhammad-etal-2025-brighter
%X 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.
%R 10.18653/v1/2025.acl-long.436
%U https://aclanthology.org/2025.acl-long.436/
%U https://doi.org/10.18653/v1/2025.acl-long.436
%P 8895-8916
Markdown (Informal)
[BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages](https://aclanthology.org/2025.acl-long.436/) (Muhammad et al., ACL 2025)
ACL
- Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin, Jan Philip Wahle, Terry Ruas, Meriem Beloucif, Christine de Kock, Nirmal Surange, Daniela Teodorescu, Ibrahim Said Ahmad, David Ifeoluwa Adelani, Alham Fikri Aji, Felermino D. M. A. Ali, Ilseyar Alimova, Vladimir Araujo, Nikolay Babakov, Naomi Baes, Ana-Maria Bucur, Andiswa Bukula, Guanqun Cao, Rodrigo Tufiño, Rendi Chevi, Chiamaka Ijeoma Chukwuneke, Alexandra Ciobotaru, Daryna Dementieva, Murja Sani Gadanya, Robert Geislinger, Bela Gipp, Oumaima Hourrane, Oana Ignat, Falalu Ibrahim Lawan, Rooweither Mabuya, Rahmad Mahendra, Vukosi Marivate, Alexander Panchenko, Andrew Piper, Charles Henrique Porto Ferreira, Vitaly Protasov, Samuel Rutunda, Manish Shrivastava, Aura Cristina Udrea, Lilian Diana Awuor Wanzare, Sophie Wu, Florian Valentin Wunderlich, Hanif Muhammad Zhafran, Tianhui Zhang, Yi Zhou, and Saif M. Mohammad. 2025. BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8895–8916, Vienna, Austria. Association for Computational Linguistics.