Sentiment Analysis Across Multiple African Languages: A Current Benchmark
This work addresses the fragmented research in sentiment analysis for African languages by providing a benchmark, though it is incremental as it builds on existing shared task data and models.
The authors benchmarked state-of-the-art transformer models for sentiment analysis across 12 African languages, finding that models specifically developed for African languages outperformed others, and no single model performed best for all languages, with multilingual models sometimes better for low-resource cases.
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.