  This paper describes our system designed for SemEval-2023 Task 12: Sentiment
analysis for African languages. The challenge faced by this task is the
scarcity of labeled data and linguistic resources in low-resource settings. To
alleviate these, we propose a generalized multilingual system SACL-XLMR for
sentiment analysis on low-resource languages. Specifically, we design a
lexicon-based multilingual BERT to facilitate language adaptation and
sentiment-aware representation learning. Besides, we apply a supervised
adversarial contrastive learning technique to learn sentiment-spread structured
representations and enhance model generalization. Our system achieved
competitive results, largely outperforming baselines on both multilingual and
zero-shot sentiment classification subtasks. Notably, the system obtained the
1st rank on the zero-shot classification subtask in the official ranking.
Extensive experiments demonstrate the effectiveness of our system.
