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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.11926 |
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| _version_ | 1866913865670852608 |
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| author | Muhammad, Shamsuddeen Hassan Ousidhoum, Nedjma Abdulmumin, Idris Wahle, Jan Philip Ruas, Terry Beloucif, Meriem de Kock, Christine Surange, Nirmal Teodorescu, Daniela Ahmad, Ibrahim Said Adelani, David Ifeoluwa Aji, Alham Fikri Ali, Felermino D. M. A. Alimova, Ilseyar Araujo, Vladimir Babakov, Nikolay Baes, Naomi Bucur, Ana-Maria Bukula, Andiswa Cao, Guanqun Cardenas, Rodrigo Tufino Chevi, Rendi Chukwuneke, Chiamaka Ijeoma Ciobotaru, Alexandra Dementieva, Daryna Gadanya, Murja Sani Geislinger, Robert Gipp, Bela Hourrane, Oumaima Ignat, Oana Lawan, Falalu Ibrahim Mabuya, Rooweither Mahendra, Rahmad Marivate, Vukosi Panchenko, Alexander Piper, Andrew Ferreira, Charles Henrique Porto Protasov, Vitaly Rutunda, Samuel Shrivastava, Manish Udrea, Aura Cristina Wanzare, Lilian Diana Awuor Wu, Sophie Wunderlich, Florian Valentin Zhafran, Hanif Muhammad Zhang, Tianhui Zhou, Yi Mohammad, Saif M. |
| author_facet | Muhammad, Shamsuddeen Hassan Ousidhoum, Nedjma Abdulmumin, Idris Wahle, Jan Philip Ruas, Terry Beloucif, Meriem de Kock, Christine Surange, Nirmal Teodorescu, Daniela Ahmad, Ibrahim Said Adelani, David Ifeoluwa Aji, Alham Fikri Ali, Felermino D. M. A. Alimova, Ilseyar Araujo, Vladimir Babakov, Nikolay Baes, Naomi Bucur, Ana-Maria Bukula, Andiswa Cao, Guanqun Cardenas, Rodrigo Tufino Chevi, Rendi Chukwuneke, Chiamaka Ijeoma Ciobotaru, Alexandra Dementieva, Daryna Gadanya, Murja Sani Geislinger, Robert Gipp, Bela Hourrane, Oumaima Ignat, Oana Lawan, Falalu Ibrahim Mabuya, Rooweither Mahendra, Rahmad Marivate, Vukosi Panchenko, Alexander Piper, Andrew Ferreira, Charles Henrique Porto Protasov, Vitaly Rutunda, Samuel Shrivastava, Manish Udrea, Aura Cristina Wanzare, Lilian Diana Awuor Wu, Sophie Wunderlich, Florian Valentin Zhafran, Hanif Muhammad Zhang, Tianhui Zhou, Yi Mohammad, Saif M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11926 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages Muhammad, Shamsuddeen Hassan Ousidhoum, Nedjma Abdulmumin, Idris Wahle, Jan Philip Ruas, Terry Beloucif, Meriem de Kock, Christine Surange, Nirmal Teodorescu, Daniela Ahmad, Ibrahim Said Adelani, David Ifeoluwa Aji, Alham Fikri Ali, Felermino D. M. A. Alimova, Ilseyar Araujo, Vladimir Babakov, Nikolay Baes, Naomi Bucur, Ana-Maria Bukula, Andiswa Cao, Guanqun Cardenas, Rodrigo Tufino Chevi, Rendi Chukwuneke, Chiamaka Ijeoma Ciobotaru, Alexandra Dementieva, Daryna Gadanya, Murja Sani Geislinger, Robert Gipp, Bela Hourrane, Oumaima Ignat, Oana Lawan, Falalu Ibrahim Mabuya, Rooweither Mahendra, Rahmad Marivate, Vukosi Panchenko, Alexander Piper, Andrew Ferreira, Charles Henrique Porto Protasov, Vitaly Rutunda, Samuel Shrivastava, Manish Udrea, Aura Cristina Wanzare, Lilian Diana Awuor Wu, Sophie Wunderlich, Florian Valentin Zhafran, Hanif Muhammad Zhang, Tianhui Zhou, Yi Mohammad, Saif M. Computation and Language 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. |
| title | BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.11926 |