Muhammad, SH;
Abdulmumin, I;
Ayele, AA;
Ousidhoum, N;
Adelani, DI;
Yimam, SM;
Ahmad, IS;
... Arthur, S; + view all
(2023)
AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages.
In:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.
(pp. pp. 13968-13981).
Association for Computational Linguistics
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Abstract
Africa is home to over 2,000 languages from more than six language families and has the highest linguistic diversity among all continents. These include 75 languages with at least one million speakers each. Yet, there is little NLP research conducted on African languages. Crucial to enabling such research is the availability of high-quality annotated datasets. In this paper, we introduce AfriSenti, a sentiment analysis benchmark that contains a total of >110,000 tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yorùbá) from four language families. The tweets were annotated by native speakers and used in the AfriSenti-SemEval shared task 1. We describe the data collection methodology, annotation process, and the challenges we dealt with when curating each dataset. We further report baseline experiments conducted on the different datasets and discuss their usefulness.
| Type: | Proceedings paper |
|---|---|
| Title: | AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages |
| Event: | Conference on Empirical Methods in Natural Language Processing |
| ISBN-13: | 9798891760608 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://aclanthology.org/2023.emnlp-main.862.pdf |
| Language: | English |
| Additional information: | This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10188836 |
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