In this paper, we have performed sentiment analysis on three African languages (Hausa, Swahili, and Yoruba). We used various deep learning and traditional models paired with a vectorizer for classification and data -preprocessing. We have also used a few data oversampling methods to handle the imbalanced text data. Thus, we could analyze the performance of those models in all the languages by using weighted and macro F1 scores as evaluation metrics.