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<p>This study explores the application of text mining and deep learning techniques in identifying psychological pressure experienced by college students during the employment process. Through sentiment analysis, the performance of BERT, CNN, and a hybrid BERT-CNN model is evaluated. The results show that the hybrid model achieves superior performance in accuracy, F1 score, and recall, effectively identifying emotional signals indicative of psychological stress. While the approach does not directly alleviate stress, it offers a valuable tool for early detection, enabling institutions to develop targeted mental health support and employment guidance. This study provides a technical foundation for data-driven psychological assessment and student support services.</p>

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The application of text mining and deep learning in identifying the psychological pressure of college students in employment

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月20日

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  • Guanlan Chen

摘要

This study explores the application of text mining and deep learning techniques in identifying psychological pressure experienced by college students during the employment process. Through sentiment analysis, the performance of BERT, CNN, and a hybrid BERT-CNN model is evaluated. The results show that the hybrid model achieves superior performance in accuracy, F1 score, and recall, effectively identifying emotional signals indicative of psychological stress. While the approach does not directly alleviate stress, it offers a valuable tool for early detection, enabling institutions to develop targeted mental health support and employment guidance. This study provides a technical foundation for data-driven psychological assessment and student support services.