We describe our contribution to the SemEVAl 2023 AfriSenti-SemEval shared task, where we tackle the task of sentiment analysis in 14 different African languages. We develop both monolingual and multilingual models under a full supervised setting (subtasks A and B). We also develop models for the zero-shot setting (subtask C). Our approach involves experimenting with transfer learning using six language models, including further pertaining of some of these models as well as a final finetuning stage. Our best performing models achieve an F1-score of 70.36 on development data and an F1-score of 66.13 on test data. Unsurprisingly, our results demonstrate the effectiveness of transfer learning and fine-tuning techniques for sentiment analysis across multiple languages. Our approach can be applied to other sentiment analysis tasks in different languages and domains.

翻译：我们描述了在SemEval 2023 AfriSenti-SemEval共享任务中的贡献，该任务涉及对14种不同的非洲语言进行情感分析。我们在全监督设置下（子任务A和B）开发了单语言和多语言模型。同时，我们还为零样本设置（子任务C）开发了模型。我们的方法包括利用六种语言模型进行迁移学习实验，并对其中部分模型进行进一步预训练以及最终的微调阶段。最佳模型在开发数据上达到了70.36的F1分数，在测试数据上达到了66.13的F1分数。不出所料，我们的结果证明了迁移学习和微调技术在跨语言情感分析中的有效性。该方法可应用于不同语言和领域的其他情感分析任务。