Adversarial Transfer Learning for Privacy-Preserving Multilingual Sentiment Detection on Social Media

In today's global digital environment, sentiment detection across multiple languages on social media is crucial for understanding public opinion, especially in multilingual societies. However, balancing model performance with user privacy remains a significant challenge in cross-lingual sentiment analysis. Existing methods often lack generalization across languages and do not adequately protect users' sensitive information, making them unsuitable for privacy-sensitive applications. To address these challenges, propose a novel framework called Privacy-Aware Adversarial Domain Adaptation (PADA), which combines adversarial transfer learning with differential privacy mechanisms. The PADA framework employs a shared feature extractor, a sentiment classifier, a domain discriminator for adversarial training, and a differential privacy module to preserve user anonymity while enabling effective multilingual sentiment learning. This method is particularly applicable to analyzing public sentiment during crises, elections, or global events by safely leveraging sentiment data from high-resource languages to enhance performance in low-resource languages. Experimental results show that proposed framework significantly improves sentiment classification accuracy across languages while maintaining strong privacy guarantees, demonstrating its effectiveness in real-world, privacy-sensitive multilingual environments.

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