AfriSenti: a twitter sentiment analysis benchmark for African languages

Par Conseil national de recherches du Canada

Téléchargement	Voir la version finale : AfriSenti: a twitter sentiment analysis benchmark for African languages (PDF, 1.4 Mio)
DOI	Trouver le DOI : https://doi.org/10.18653/v1/2023.emnlp-main.862
Auteur	Rechercher : Muhammad, Shamsuddeen; Rechercher : Abdulmumin, Idris; Rechercher : Ayele, Abinew; Rechercher : Ousidhoum, Nedjma; Rechercher : Adelani, David; Rechercher : Yimam, Seid; Rechercher : Ahmad, Ibrahim; Rechercher : Beloucif, Meriem; Rechercher : Mohammad, Saif¹; Rechercher : Ruder, Sebastian; Rechercher : Hourrane, Oumaima; Rechercher : Jorge, Alipio; Rechercher : Brazdil, Pavel; Rechercher : Ali, Felermino; Rechercher : David, Davis; Rechercher : Osei, Salomey; Rechercher : Shehu-Bello, Bello; Rechercher : Lawan, Falalu; Rechercher : Gwadabe, Tajuddeen; Rechercher : Rutunda, Samuel; Rechercher : Belay, Tadesse; Rechercher : Messelle, Wendimu; Rechercher : Balcha, Hailu; Rechercher : Chala, Sisay; Rechercher : Gebremichael, Hagos; Rechercher : Opoku, Bernard; Rechercher : Arthur, Stephen
Affiliation	Conseil national de recherches du Canada. Technologies numériques
Format	Texte, Article
Conférence	2023 Conference on Empirical Methods in Natural Language Processing, December 6-10, 2023, Singapore
Résumé	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 >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.
Date de publication	2023-12-06
Maison d’édition	Association for Computational Linguistics
Licence	Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/deed.fr
Dans	Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (6 décembre 2023) : 13968–13981. https://doi.org/10.18653/v1/2023.emnlp-main.
Langue	anglais
Publications évaluées par des pairs	Oui
Exporter la notice	Exporter en format RIS
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Identificateur de l’enregistrement	31b058c0-af49-46ba-8e74-e1c67c14a16b
Enregistrement créé	2024-01-08
Enregistrement modifié	2024-01-09

Date de modification :: 2024-07-25