Cross-Lingual Sentiment Classification with Attention-Based Multilingual Language Models

Abstract

Cross-lingual sentiment classification has become a critical capability for organizations that need to interpret subjective language across linguistically diverse digital environments. This paper examines the problem from a system-level perspective, focusing on attention-based multilingual language models as infrastructural components rather than isolated algorithms. The discussion covers architectural foundations, cross-lingual transfer dynamics, deployment trade-offs, robustness, fairness, governance, and sustainability. Multilingual pretrained models built on transformer self-attention have improved zero-shot and few-shot sentiment transfer, but they introduce structural tensions between parameter sharing, language coverage, inference cost, and downstream accuracy. The paper argues that successful deployment requires coordinated attention to training data provenance, model lifecycle management, computational efficiency, and evaluation practices that account for linguistic and cultural diversity. It further examines how governance mechanisms, including model documentation, auditing, and participatory language community engagement, can address disparities across high-resource and low-resource languages. Cross-domain illustrations from consumer analytics, public health monitoring, and content moderation show that sentiment classification is not a context-free task. Instead, it is embedded in social, economic, and regulatory systems. The paper concludes by identifying forward-looking research directions that integrate architectural innovation, infrastructure planning, and policy development for more robust and equitable multilingual sentiment systems.