Data annotation is a time-consuming and labor-intensive process in classification tasks. Recently, numerousstudies have explored the few-shot learning approach using meta-learning, particularly the MAMLalgorithm. Most research aimed at improving MAML has focused on image classification rather thantext data, and the proposed enhancements often involve complex models that require significant processingresources. Furthermore, there is a notable scarcity of research attempting to apply few-shotlearning methodologies to the Arabic language. This research paper aims to enhance the performanceof the Model-Agnostic Meta-Learning (MAML) algorithm in the domain of few-shot sentiment analysis,with a specific focus on the Arabic language, which suffers from resource scarcity and a lack of multi-domain labeled datasets. This paper addresses two primary challenges: the instability of the MAMLalgorithm during training, and the importance of measuring divergence between training domains. Toimprove training stability without requiring substantial processing resources, we propose using CosineAnnealing to schedule the learning rate in the outer loop of MAML. Additionally, we present a significantempirical finding demonstrating that the homogeneity of training domains has a substantialimpact on MAML performance. The validity of these contributions is verified through extensive experimentson sentiment analysis datasets in both English and Arabic, including the Amazon Reviewsdataset and a multi-domain Arabic dataset compiled from several other research studies, processed,and formatted to be suitable for the MAML algorithm. The results demonstrate the effectiveness ofthe proposed method in improving the stability and performance of MAML and underscore the importanceof training domain homogeneity in few-shot learning scenarios with low processing resources.Keywords: Meta Learning, Sentiment Analysis, Few-Shot Learning, Multi Domain Learning, Domain Homogeneity,Cosine Annealing.