Artificial Intelligence for Emotion Recognition in Low-Resource Language Social Media Texts: A Comprehensive Review

Emotion recognition in social media texts has witnessed significant advancements with the integration of artificial intelligence (AI) techniques. This review explores the utility of AI methods specifically in the framework of lowresource languages, focusing on challenges and methodologies in emotion recognition. Low-resource languages pose unique challenges such as limited annotated data and linguistic resources, which necessitate innovative AI approaches for effective emotion detection. We survey existing literature on traditional machine learning and advanced deep learning models tailored for languages of low-resource, highlighting their strengths and limitations. The review discusses key strategies including transfer learning, data augmentation, and cross-lingual embeddings to mitigate these challenges. Moreover, we identify emerging trends and future research directions to enhance AIdriven emotion recognition in language having low-resource social media texts.