Emotion detection in text-based content, such as opinions, comments, and textual interactions, holds pivotal significance in enabling computers to comprehend human emotions. This symbiotic understanding between machines and human languages, powered by technological advancements like Natural Language Processing and artificial intelligence, has revolutionized the dynamics of human-computer interaction. The complexity of emotion detection, although challenging, has surged in importance across diverse domains, encompassing customer service, healthcare, and surveillance of social media interactions. Within the realm of text analysis, the quest for accurate emotion detection necessitates a profound exploration of cutting-edge methodologies. This pursuit is further intensified by the imperative to fortify models against adversarial attacks, a pressing concern in deep learning-based approaches. To address this critical challenge, this paper introduces a pioneering technique—adversarial transfer learning—specifically tailored for emotion classification in text analysis. By infusing adversarial training into the model architecture, the proposed approach emerges a solution that not only mitigates the vulnerabilities of existing methods but also fortifies the model against adversarial intrusions. In realizing the potential of the proposed approach, a diverse array of datasets is harnessed for comprehensive training. The empirical results vividly demonstrate the efficacy of this approach, showcasing its superior performance when compared to state-of-the-art methodologies. Notably, the suggested approach yields in advancements in classification accuracy. In particular, the deployment of the Adversarial transfer learning methodology has increased in accuracy of 17.35%. This study, therefore, encapsulates a dual achievement: the introduction of an innovative approach that leverages adversarial transfer learning for emotion classification, and the subsequent empirical validation of its unparalleled efficiency. The implications reverberate across multiple sectors, extending the horizons of accurate emotion detection and laying a foundation for the next stride in human-computer interaction and emotion analysis.