Data Classification of Mental Health and Personality Evaluation Based on Network Deep Learning

Abstract

In real life, people are at a risk of encountering various negative emotions all the time, and in the case of long-term negative emotions, it is easy to fall into a state of depression. However, in the current mental health system, the diagnosis of the depressive state of a client usually requires a doctor or a consultant to conduct face-to-face or video consultation with the client, which is time consuming and labor intensive. Therefore, it is necessary to adopt IT for mental health monitoring and personality data analysis. In order to achieve better results in identifying the students’ mental health problems, this paper attempts to use multiple data sources, proposes an algorithm for identifying mental health problems based on multiple data sources, and uses the data on students’ mental states provided in psychology as labels to improve the shortcomings brought about by the questionnaire approach. To further optimise the model identification results, this paper proposes a mental health problem identification algorithm based on the DeepPsy model. A 2D-CNN was used to extract the online pattern of a day, an LSTM network was used to capture the temporal dependency between days, and a deep learning network was designed to combine the underlying features with the online trajectory pattern. Experiments showed an accuracy of 0.71, a recall of 0.75, and an F1-Measure of 0.72, and were able to identify 75% of students with mental health problems.