Robust Sentiment Analysis in Service Systems: Enhancing XLNet with Adversarial Training

In service-intensive industries, sentiment analysis plays a pivotal role in understanding customer experiences and driving data-informed decisions. However, these systems are vulnerable to adversarial text manipulations that undermine their reliability. This paper investigates the use of XLNet, a permutation-based transformer model, for sentiment analysis in service contexts and evaluates its robustness under adversarial attacks. We propose a defense mechanism based on adversarial training with embedding-level augmentation. Experiments on diverse benchmark datasets show that while XLNet performs well on clean data, adversarial training significantly enhances robustness without sacrificing accuracy. Our findings offer actionable insights for building trustworthy sentiment analysis pipelines in service systems.