  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 Yor\`ub\'a) from four language families.
The tweets were annotated by native speakers and used in the AfriSenti-SemEval
shared task (The AfriSenti Shared Task had over 200 participants. See website
at 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 different
datasets and discuss their usefulness.
