The manually annotated emotion recognition dataset, curated in collaboration with local communities, consist of multi-labeled instances drawn from diverse sources, including speeches, social media, news, literature, and reviews. Each instance is labeled by fluent speakers and annotated with six emotion classes: joy, sadness, anger, fear, surprise, disgust, and neutral.
Language(s)
Spanish
English
Dataset description link
Year
2025
Domain
Diverse
Annotations
Each instance is labeled by fluent speakers and annotated with six emotion classes: joy, sadness, anger, fear, surprise, disgust, and neutral.
Format
csv
Data access
Registration
Publication
Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin, Seid Muhie Yimam, Jan Philip Wahle, Terry Lima Ruas, Meriem Beloucif, Christine De Kock, Tadesse Destaw Belay, Ibrahim Said Ahmad, Nirmal Surange, Daniela Teodorescu, David Ifeoluwa Adelani, Alham Fikri Aji, Felermino Dario Mario Ali, Vladimir Araujo, Abinew Ali Ayele, Oana Ignat, Alexander Panchenko, Yi Zhou, and Saif Mohammad. 2025. SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 2558–2569, Vienna, Austria. Association for Computational Linguistics.
Publication link
NLP Topic
Number of units
3875
Training set size
1996
Test set size
1695
Development set size
184
