International Journal of Management

Cross-national sentiment analysis models aim to evaluate emotional tone, public opinion, and behavioral patterns across multiple linguistic and cultural contexts. Unlike monolingual sentiment models trained on single-language datasets, multi-country sentiment models integrate multilingual corpora, cultural lexicons, machine translation pipelines, embedding alignment, and cross-regional semantic calibration. This paper analyzes theoretical foundations, model architectures, and real-world challenges in constructing scalable global sentiment systems. Using conceptual models and hypothetical datasets covering five countries, results illustrate accuracy variations caused by cultural idioms, lexicon bias, sarcasm interpretation, political framing differences, and morphological complexity. A Global Multi-Country Sentiment Model Framework is proposed for future research and real-time global analytics.