Vol. 2 No. issue 10,page1905-1922 (2025): Recent Advances in Multilingual Sentiment Analysis for Social Media: A Systematic Literature Review of Deep Learning and Transformer-Based Approaches

Digital communication has evolved a context that allows us to capture different languages and trials in different cultures, leading to the development of multilingual sentiment classification. But the models that are based on transformer technology such as BERT and GPT-encouraged trials are more relevant and successful in interpreting the emotional tone of the text of various linguistic paradigms. This study employed a PRISM 2020 structural method and included 15 recorded sample literatures to curate narrative synthesis. Advanced deep-learning procedures and transformer-based models always seem to outperform convention language processing models regarding cross-lingual detection analysis. Despite numerous advantages, transformer-based tools also refer to limited annotation for limited languages and disparities in dialect modulation. Application of emojis, complex connotations, GIF, and abbreviations also present high-order barriers for transformer tools to conduct cross-lingual analysis. Overall, this report highlights the effectiveness of transformer-based approaches in gaining more cohesive and culturally explainable multilingual evaluation process which enable future social media-based natural language processing.

Keywords: Social Networking Sites, Multilingual Sentiment Analysis, Natural Language Processing (NLP), Transformer-based Models, Deep Learning