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Automatic Recognition of Student Emotions Based on Deep Neural Network and Its Application in Depression Detection

The mental health rating scales combined with psychological expert consultation are easily influenced by subjective factors from psychological experts and lack objectivity and scientificalness. Due to the serious influence of depression tendency on learning and living of college students,
 a novel DNN network model framework based on context emotion information is designed to achieve the automatic auxiliary emotion classification. More corpus training sample can be received by heightening the sample length without reducing samples' number. Firstly, the existing sample feature
 is input into the recognition model to encode depression related features, and then the MADN features of the samples in the two adjacent segments are input into the above trained model in order for fine-tuning and optimization. Compared with the existing optimal method, the proposed model
 improves the recognition accuracy in the diagnosis of depression. From the analysis of the experimental results, it is known that deep learning network can monitor the emotional state of college students with high precision, which can accurately identify the patients with depression. The deep
 learning model can take effective measures to prevent the depression of college students, and discover the depression of college students, alleviate and treat the depression of college students, reduce the depression rate of college students.