dataset · Zenodo (CERN European Organization for Nuclear Research)
SARCSenti is a newly developed, tone-marked Yoruba dataset designed for affective natural language processing, specifically for binary sarcasm detection and ternary sentiment classification. It comprises 1,507 Yoruba headline records, each annotated for both sarcasm (non-sarcastic or sarcastic) and sentiment (negative, neutral, or positive). The dataset includes 1,334 records derived from BBC Yoruba and 173 Yoruba translations from another news headlines dataset. This resource aims to support research in Yoruba and African low-resource natural language processing, sarcasm detection, sentiment analysis, affective computing, cross-task learning, and the processing of tonal languages. Access to the full dataset is currently restricted due to copyright considerations for some source material.
Understanding the nuances of human language, including sarcasm and sentiment, is vital for effective digital communication. This dataset provides a crucial resource for developing artificial intelligence that can better interpret the Yoruba language, especially its tonal characteristics, thereby improving technology's ability to process and respond to African languages more accurately.
This dataset is an early-stage research tool, providing foundational data for developing advanced natural language processing applications for Yoruba. It could enable future tools for sentiment analysis, content moderation, or intelligent chatbots in Yoruba. Potential users include researchers and developers working on African language technologies, as well as organisations needing to analyse public opinion or detect specific tones in Yoruba text. Full commercialisation pathways are not indicated, as the dataset itself is a research enabler.
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SARCSenti is a tone-marked Yoruba dataset developed for affective natural language processing, specifically binary sarcasm detection and ternary sentiment classification. The analytical release contains 1,507 Yoruba headline records, annotated for both sarcasm and sentiment. Sarcasm labels are non-sarcastic (0) and sarcastic (1), while sentiment labels are negative (0), neutral (1), and positive (2). The dataset contains 1,334 BBC Yoruba-derived records and 173 Yoruba translations derived from the Misra/Kaggle News Headlines Dataset for Sarcasm Detection. Record-level provenance is provided in the source field. Three records from the original research workbook were excluded from the analytical release because of missing or invalid numeric sentiment labels. SARCSenti was developed as part of research conducted in the Department of Computer Science, Lead City University, Ibadan, Nigeria. The dataset is intended to support research on Yoruba and African low-resource NLP, sarcasm detection, sentiment analysis, affective computing, cross-task learning, and the processing of tonal languages. Yoruba text is provided in UTF-8 and normalized using Unicode NFC. The accompanying README and data dictionary provide information on dataset structure, labels, provenance, and usage. Access to the full-text dataset is restricted because a substantial portion contains source-derived BBC Yoruba headline text for which open redistribution rights have not yet been confirmed. Research access may be considered subject to applicable source terms and copyright restrictions.
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DOI: 10.5281/zenodo.22308685
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