Psychological Analysis of College Students?? Anxiety Based on Domain Comparison Adaptive Model

Abstract

The anxiety detection task involves the utilization of natural language processing techniques to analyze users?? content posted on social media. To promote research on anxiety detection among college students, a Weibo dataset has been developed. Addressing the high cost of data annotation, this paper adopts an unsupervised approach to analyze zero-shot data under the context of public health emergencies, and proposes an adaptive model called domain adaptive network based on contrastive learning (DAN-CL). DAN-CL, based on a pre-trained language model, integrates source domain text and target domain text into a knowledge distillation-based adversarial network for domain adaptive training, enabling knowledge transfer of the model. It also employs contrastive learning methods to enhance the generalization ability of the model. Benchmark experimental results show that DAN-CL outperforms existing comparative models, and ablation experiments further validate the effectiveness of different components within the model. Additionally, an analysis of the psychological status of college students reveals that changes in their anxiety levels align with the trends of sudden public health events. Under the influence of disease progression and the surrounding environment, their anxiety levels increase significantly. A specific analysis of the causes of anxiety and the countermeasures provides theoretical support for addressing college students?? mental health challenges during and after sudden public health events.

Keywords

• anxiety psychology; domain adaptive; contrastive learning; sentiment analysis