  This report describes GMU's sentiment analysis system for the SemEval-2023
shared task AfriSenti-SemEval. We participated in all three sub-tasks:
Monolingual, Multilingual, and Zero-Shot. Our approach uses models initialized
with AfroXLMR-large, a pre-trained multilingual language model trained on
African languages and fine-tuned correspondingly. We also introduce augmented
training data along with original training data. Alongside finetuning, we
perform phylogeny-based adapter tuning to create several models and ensemble
the best models for the final submission. Our system achieves the best F1-score
on track 5: Amharic, with 6.2 points higher F1-score than the second-best
performing system on this track. Overall, our system ranks 5th among the 10
systems participating in all 15 tracks.
