We present the findings of SemEval-2023 Task 12, a shared task on sentiment
analysis for low-resource African languages using Twitter dataset. The task
featured three subtasks; subtask A is monolingual sentiment classification with
12 tracks which are all monolingual languages, subtask B is multilingual
sentiment classification using the tracks in subtask A and subtask C is a
zero-shot sentiment classification. We present the results and findings of
subtask A, subtask B and subtask C. We also release the code on github. Our
goal is to leverage low-resource tweet data using pre-trained Afro-xlmr-large,
AfriBERTa-Large, Bert-base-arabic-camelbert-da-sentiment (Arabic-camelbert),
Multilingual-BERT (mBERT) and BERT models for sentiment analysis of 14 African
languages. The datasets for these subtasks consists of a gold standard
multi-class labeled Twitter datasets from these languages. Our results
demonstrate that Afro-xlmr-large model performed better compared to the other
models in most of the languages datasets. Similarly, Nigerian languages: Hausa,
Igbo, and Yoruba achieved better performance compared to other languages and
this can be attributed to the higher volume of data present in the languages.

通过 SemEval-2023 任务 12，使用 Twitter 数据组进行低资源非洲语言情感分析。使用预训练的 Afro-xlmr-large、AfriBERTa-Large、Bert-base-arabic-camelbert-da-sentiment (阿拉伯骆驼鸟 Bert)、Multilingual-BERT 和 BERT 模型对 14 种非洲语言进行情感分析。结果表明 Afro-xlmr-large 模型在大多数语言中表现更好，尼日利亚语言：豪萨语、伊博语和约鲁巴语相对于其他语言表现更好。