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AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages

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arxiv 2302.08956 v5 pith:SKI7J5AK submitted 2023-02-17 cs.CL

Shamsuddeen Hassan Muhammad , Idris Abdulmumin , Abinew Ali Ayele , Nedjma Ousidhoum , David Ifeoluwa Adelani , Seid Muhie Yimam , Ibrahim Sa'id Ahmad , Meriem Beloucif
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Saif M. Mohammad Sebastian Ruder Oumaima Hourrane Pavel Brazdil Felermino Dário Mário António Ali Davis David Salomey Osei Bello Shehu Bello Falalu Ibrahim Tajuddeen Gwadabe Samuel Rutunda Tadesse Belay Wendimu Baye Messelle Hailu Beshada Balcha Sisay Adugna Chala Hagos Tesfahun Gebremichael Bernard Opoku Steven Arthur
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classification cs.CL
keywords languagesafricanafrisentiafrisenti-semevalanalysisannotatedarabicbenchmark
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English

    cs.CL 2024-12 conditional novelty 7.0 of 10

    BESSTIE is the first sentiment and sarcasm benchmark for three varieties of English, and models trained on it score lowest on Indian English.

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