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We describe our contribution to the SemEVAl 2023 AfriSenti-SemEval sharedtask, where we tackle the task of sentiment analysis in 14 different Africanlanguages. We develop both monolingual and multilingual models under a fullsupervised setting (subtasks A and B). We also develop models for the zero-shotsetting (subtask C). Our approach involves experimenting with transfer learningusing six language models, including further pertaining of some of these modelsas well as a final finetuning stage. Our best performing models achieve anF1-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 learningand fine-tuning techniques for sentiment analysis across multiple languages.Our approach can be applied to other sentiment analysis tasks in differentlanguages and domains.

UBC-DLNLP at SemEval-2023 Task 12: Impact of Transfer Learning on African Sentiment Analysis