Cross-Lingual Sentiment Analysis in Low-Resource Languages: A Recent Review on Tasks, Methods and Challenges

Cross-lingual sentiment analysis (CLSA) has become increasingly important in natural language processing and machine learning, enabling the understanding of opinions across diverse linguistic communities, particularly in low-resource languages (LRLs). Despite growing attention, persistent challenges such as limited annotated data, semantic misalignment, and cultural variation in sentiment expression continue to hinder progress. This systematic literature review (SLR) examines recent developments by analyzing the tasks, methods, and challenges reported in CLSA studies focused on LRLs. Following the PRISMA 2020 framework, a comprehensive search was conducted across major databases, including Scopus, IEEE Xplore, SpringerLink, Elsevier, and Google Scholar, covering studies published between 2021 and 2025. After applying inclusion and exclusion criteria, 27 studies were selected for analysis. The findings reveal that while polarity detection remains the dominant sentiment analysis task, emerging directions such as aspect-based sentiment analysis (ABSA), emotion detection, and hate speech recognition are gaining traction. Methodologically, most studies rely on multilingual pre-trained language models (PLMs), supplemented by machine translation, transfer learning, few-shot learning, and hybrid approaches. However, key challenges remain, including the scarcity of high-quality datasets, instability of few-shot performance, difficulties in handling dialectal variation, bias in PLMs, and the lack of standardized evaluation benchmarks. This review concludes by emphasizing the need for more culturally grounded tasks, adaptive hybrid frameworks, and fairness-aware evaluation practices to build robust cross-lingual frameworks and richer linguistic resources for underrepresented languages.