@inproceedings{muhammad-etal-2023-afrisenti,
title = "{A}fri{S}enti: A {T}witter Sentiment Analysis Benchmark for {A}frican Languages",
author = "Muhammad, Shamsuddeen and
Abdulmumin, Idris and
Ayele, Abinew and
Ousidhoum, Nedjma and
Adelani, David and
Yimam, Seid and
Ahmad, Ibrahim and
Beloucif, Meriem and
Mohammad, Saif and
Ruder, Sebastian and
Hourrane, Oumaima and
Jorge, Alipio and
Brazdil, Pavel and
Ali, Felermino and
David, Davis and
Osei, Salomey and
Shehu-Bello, Bello and
Lawan, Falalu and
Gwadabe, Tajuddeen and
Rutunda, Samuel and
Belay, Tadesse and
Messelle, Wendimu and
Balcha, Hailu and
Chala, Sisay and
Gebremichael, Hagos and
Opoku, Bernard and
Arthur, Stephen",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.862",
doi = "10.18653/v1/2023.emnlp-main.862",
pages = "13968--13981",
abstract = "Africa is home to over 2,000 languages from over six language families and has the highest linguistic diversity among all continents. This includes 75 languages with at least one million speakers each. Yet, there is little NLP research conducted on African languages. Crucial in 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 {\textgreater}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 Yoruba) from four language families. The tweets were annotated by native speakers and used in the AfriSenti-SemEval shared task (with over 200 participants, see website: https://afrisenti-semeval.github.io). 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 AfriSenti datasets and discuss their usefulness.",
}
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<abstract>Africa is home to over 2,000 languages from over six language families and has the highest linguistic diversity among all continents. This includes 75 languages with at least one million speakers each. Yet, there is little NLP research conducted on African languages. Crucial in 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 \textgreater110,000 tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yoruba) from four language families. The tweets were annotated by native speakers and used in the AfriSenti-SemEval shared task (with over 200 participants, see website: https://afrisenti-semeval.github.io). 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 AfriSenti datasets and discuss their usefulness.</abstract>
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%0 Conference Proceedings
%T AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages
%A Muhammad, Shamsuddeen
%A Abdulmumin, Idris
%A Ayele, Abinew
%A Ousidhoum, Nedjma
%A Adelani, David
%A Yimam, Seid
%A Ahmad, Ibrahim
%A Beloucif, Meriem
%A Mohammad, Saif
%A Ruder, Sebastian
%A Hourrane, Oumaima
%A Jorge, Alipio
%A Brazdil, Pavel
%A Ali, Felermino
%A David, Davis
%A Osei, Salomey
%A Shehu-Bello, Bello
%A Lawan, Falalu
%A Gwadabe, Tajuddeen
%A Rutunda, Samuel
%A Belay, Tadesse
%A Messelle, Wendimu
%A Balcha, Hailu
%A Chala, Sisay
%A Gebremichael, Hagos
%A Opoku, Bernard
%A Arthur, Stephen
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F muhammad-etal-2023-afrisenti
%X Africa is home to over 2,000 languages from over six language families and has the highest linguistic diversity among all continents. This includes 75 languages with at least one million speakers each. Yet, there is little NLP research conducted on African languages. Crucial in 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 \textgreater110,000 tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yoruba) from four language families. The tweets were annotated by native speakers and used in the AfriSenti-SemEval shared task (with over 200 participants, see website: https://afrisenti-semeval.github.io). 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 AfriSenti datasets and discuss their usefulness.
%R 10.18653/v1/2023.emnlp-main.862
%U https://aclanthology.org/2023.emnlp-main.862
%U https://doi.org/10.18653/v1/2023.emnlp-main.862
%P 13968-13981
Markdown (Informal)
[AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages](https://aclanthology.org/2023.emnlp-main.862) (Muhammad et al., EMNLP 2023)
ACL
- Shamsuddeen Muhammad, Idris Abdulmumin, Abinew Ayele, Nedjma Ousidhoum, David Adelani, Seid Yimam, Ibrahim Ahmad, Meriem Beloucif, Saif Mohammad, Sebastian Ruder, Oumaima Hourrane, Alipio Jorge, Pavel Brazdil, Felermino Ali, Davis David, Salomey Osei, Bello Shehu-Bello, Falalu Lawan, Tajuddeen Gwadabe, et al.. 2023. AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 13968–13981, Singapore. Association for Computational Linguistics.