@inproceedings{belay-etal-2026-enhancing,
title = "Enhancing Multi-Label Emotion Analysis and Corresponding Intensities for {E}thiopian Languages",
author = "Belay, Tadesse Destaw and
Gete, Dawit Ketema and
Ayele, Abinew Ali and
Kolesnikova, Olga and
Ameer, Iqra and
Sidorov, Grigori and
Yimam, Seid Muhie",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.639/",
doi = "10.63317/4a4fd8aj2q2a",
pages = "8056--8075",
abstract = "Developing and integrating emotion-understanding models are essential for a wide range of human- computer interaction tasks, including customer feedback analysis, marketing research, and social media monitoring. Given that users often express multiple emotions simultaneously within a single instance, annotating emotion datasets in a multi-label format is critical for capturing this complexity. The EthioEmo dataset, a multilingual and multi-label emotion dataset for Ethiopian languages, lacks emotion intensity annotations, which are crucial for distinguishing varying degrees of emotion, as not all emotions are expressed with the same intensity. We extend the EthioEmo dataset to address this gap by adding emotion intensity annotations. Furthermore, we benchmark state-of-the-art encoder-only Pretrained Language Models (PLMs) and Large Language Models (LLMs) on this enriched dataset. Our results demonstrate that African-centric encoder-only models consistently outperform open-source LLMs, highlighting the importance of culturally and linguistically tailored small models in emotion understanding. Incorporating an emotion-intensity feature for multi-label emotion classification yields better performance. The data is available at \url{https://huggingface.co/datasets/Tadesse/EthioEmo-intensities}."
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<abstract>Developing and integrating emotion-understanding models are essential for a wide range of human- computer interaction tasks, including customer feedback analysis, marketing research, and social media monitoring. Given that users often express multiple emotions simultaneously within a single instance, annotating emotion datasets in a multi-label format is critical for capturing this complexity. The EthioEmo dataset, a multilingual and multi-label emotion dataset for Ethiopian languages, lacks emotion intensity annotations, which are crucial for distinguishing varying degrees of emotion, as not all emotions are expressed with the same intensity. We extend the EthioEmo dataset to address this gap by adding emotion intensity annotations. Furthermore, we benchmark state-of-the-art encoder-only Pretrained Language Models (PLMs) and Large Language Models (LLMs) on this enriched dataset. Our results demonstrate that African-centric encoder-only models consistently outperform open-source LLMs, highlighting the importance of culturally and linguistically tailored small models in emotion understanding. Incorporating an emotion-intensity feature for multi-label emotion classification yields better performance. The data is available at https://huggingface.co/datasets/Tadesse/EthioEmo-intensities.</abstract>
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%0 Conference Proceedings
%T Enhancing Multi-Label Emotion Analysis and Corresponding Intensities for Ethiopian Languages
%A Belay, Tadesse Destaw
%A Gete, Dawit Ketema
%A Ayele, Abinew Ali
%A Kolesnikova, Olga
%A Ameer, Iqra
%A Sidorov, Grigori
%A Yimam, Seid Muhie
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F belay-etal-2026-enhancing
%X Developing and integrating emotion-understanding models are essential for a wide range of human- computer interaction tasks, including customer feedback analysis, marketing research, and social media monitoring. Given that users often express multiple emotions simultaneously within a single instance, annotating emotion datasets in a multi-label format is critical for capturing this complexity. The EthioEmo dataset, a multilingual and multi-label emotion dataset for Ethiopian languages, lacks emotion intensity annotations, which are crucial for distinguishing varying degrees of emotion, as not all emotions are expressed with the same intensity. We extend the EthioEmo dataset to address this gap by adding emotion intensity annotations. Furthermore, we benchmark state-of-the-art encoder-only Pretrained Language Models (PLMs) and Large Language Models (LLMs) on this enriched dataset. Our results demonstrate that African-centric encoder-only models consistently outperform open-source LLMs, highlighting the importance of culturally and linguistically tailored small models in emotion understanding. Incorporating an emotion-intensity feature for multi-label emotion classification yields better performance. The data is available at https://huggingface.co/datasets/Tadesse/EthioEmo-intensities.
%R 10.63317/4a4fd8aj2q2a
%U https://aclanthology.org/2026.lrec-1.639/
%U https://doi.org/10.63317/4a4fd8aj2q2a
%P 8056-8075
Markdown (Informal)
[Enhancing Multi-Label Emotion Analysis and Corresponding Intensities for Ethiopian Languages](https://aclanthology.org/2026.lrec-1.639/) (Belay et al., LREC 2026)
ACL