Sentiment Analysis Across Multiple African Languages: A Current
  Benchmark
Saurav K. Aryal Howard Prioleau Surakshya Aryal
Sentiment analysis is a fundamental and valuable task in NLP. However, due to
limitations in data and technological availability, research into sentiment
analysis of African languages has been fragmented and lacking. With the recent
release of the AfriSenti-SemEval Shared Task 12, hosted as a part of The 17th
International Workshop on Semantic Evaluation, an annotated sentiment analysis
of 14 African languages was made available. We benchmarked and compared current
state-of-art transformer models across 12 languages and compared the
performance of training one-model-per-language versus
single-model-all-languages. We also evaluated the performance of standard
multilingual models and their ability to learn and transfer cross-lingual
representation from non-African to African languages. Our results show that
despite work in low resource modeling, more data still produces better models
on a per-language basis. Models explicitly developed for African languages
outperform other models on all tasks. Additionally, no one-model-fits-all
solution exists for a per-language evaluation of the models evaluated.
Moreover, for some languages with a smaller sample size, a larger multilingual
model may perform better than a dedicated per-language model for sentiment
classification.