Sentiment analysis for code-mixed low-resource languages: a systematic review of approaches, techniques, applications, challenges, and future directions

Abstract

Abstract With the rapid proliferation of user-generated content across internet-based platforms, sentiment analysis has become an essential tool for understanding public opinion. However, sentiment analysis on code-mixed, low-resource languages presents significant challenges due to limited annotated data, linguistic complexity, and resource constraints. This systematic literature review (SLR) comprehensively examines recent developments in sentiment analysis for code-mixed and low-resource languages, with a focus on studies published between 2017 and 2025. The review investigates a wide range of approaches, including deep learning models, transfer learning strategies, and pre-trained language models, evaluating their effectiveness in low-resource and code-mixed contexts. Findings indicate that transfer learning and transformer-based architectures are increasingly preferred due to their superior performance and reduced reliance on large annotated datasets. Social media is identified as the dominant data source, followed by reviews from e-commerce and entertainment domains. Despite these advancements, significant challenges persist, including the scarcity of high-quality labeled data, high computational costs, and limited generalization across languages and domains. This review not only synthesizes current methodologies and applications but also proposes a conceptual framework for future research. It offers actionable insights for scholars seeking to develop efficient, scalable sentiment analysis systems tailored to code-mixed and low-resource language scenarios.