Liu Junli, Wang Haoyuan
School of Marxism, Xi'an Technological University, Xi'an, China.
Faculty of Science, McMaster University, Hamilton, ON, Canada.
Front Psychol. 2022 Jun 17;13:898609. doi: 10.3389/fpsyg.2022.898609. eCollection 2022.
Understanding students' psychological pressure and bad emotional reaction can solve psychological problems as soon as possible and avoid affecting students' normal study life. With the improvement of global scientific and technological strength, and the step-by-step in-depth research on deep learning and computational intelligence optimization. Now, we have enough conditions to build a psychological and emotional data set for the field of education, and build a mental health stress detection model with emotional analysis function. In addition, a variety of experimental methods are used for comparison, which shows the superior performance of the model in practical application scenarios. The results show that: (1) the data set constructed for the model is reasonable. Psychological stress test shows that the tested college students are in good health and have no positive performance. Schools need to pay special attention to obsessive-compulsive disorder and interpersonal sensitivity, and the average values of both indicators are higher than 0.9. (2) For the optimization of ant colony algorithm (ACO) computational intelligence, both the stability and the average execution time of the algorithm are obviously higher than those of other algorithms. This model has obvious performance advantages after using this algorithm. (3) Using loss function value to measure the difference between simulated emotion analysis and real value. The difference of most emotion tests is less than 3%; the accuracy difference between sadness and fear is about 7%. Although the final results prove the feasibility of this method, there are still some shortcomings to be optimized.
了解学生的心理压力和不良情绪反应，能够尽快解决心理问题，避免影响学生正常的学习生活。随着全球科技实力的提升，以及对深度学习和计算智能优化的逐步深入研究。如今，我们有足够的条件构建教育领域的心理和情感数据集，并构建具有情感分析功能的心理健康压力检测模型。此外，运用多种实验方法进行比较，结果表明该模型在实际应用场景中具有优越的性能。结果显示：（1）为该模型构建的数据集合理。心理压力测试表明，受试大学生身体健康状况良好，无阳性表现。学校需特别关注强迫症和人际敏感问题，这两项指标的平均值均高于0.9。（2）对于蚁群算法（ACO）计算智能的优化，该算法的稳定性和平均执行时间均明显高于其他算法。使用该算法后，此模型具有明显的性能优势。（3）使用损失函数值来衡量模拟情感分析与真实值之间的差异。大多数情感测试的差异小于3%；悲伤和恐惧之间的准确率差异约为7%。虽然最终结果证明了该方法的可行性，但仍存在一些有待优化的不足之处。
