Abstract
Aiming at the intelligent needs of psychological state assessment of university students, the text information-based psychological problem identification approach is investigated in the paper. This approach uses the text of student forums within universities as the database and introduces the convolutional neural network (CNN) model in deep learning, which contains a convolutional layer, a pooling layer, and a fully connected layer. After the convolution is completed, the convolution result is delinearized by the activation function, and then, pooling is performed to improve the fitting ability of the network for nonlinearities. For data processing, behavioral features, attribute features, content features, and social relationship features are extracted from text information as the input of the CNN. The psychological lexicon of expertise (LIWC) is used to enhance the efficiency of text word frequency statistics when performing text content extraction. To evaluate the performance of the proposed method, simulations are performed in the open dataset of CLPsyh2017 ReachOut Forum, and the FastText method is used as a comparison. The results show that the CNN model achieves an accuracy of 0.71 in the full-sample domain, which is significantly higher than that of the FastText model at 0.64. In the early warning evaluation of mental states, the CNN performance is better than that of FastText.
Highlights
According to the latest research statistics from the World Health Organization (WHO), mental health disorders have become the fourth most common disease worldwide
The psychological problems of college students have obvious stage characteristics, and more students are unable to detect their psychological changes in time, which leads to the deterioration of psychological problems and serious consequences [6–12]
Considering that psychological problems are difficult to detect by themselves and that students are generally resistant to psychological counseling and investigation, the textual information analysis method is used to identify psychological problems. e Internet is an important platform for students’ extracurricular spiritual life, and various social networks generate a large amount of textual information everyday, which can reflect the changes in students’ psychological status
Summary
According to the latest research statistics from the World Health Organization (WHO), mental health disorders have become the fourth most common disease worldwide. The psychological problems of college students have obvious stage characteristics, and more students are unable to detect their psychological changes in time, which leads to the deterioration of psychological problems and serious consequences [6–12]. To detect the psychological problems of college students and provide psychological help on time, the intelligent psychological state evaluation method is studied in this paper. Considering that psychological problems are difficult to detect by themselves and that students are generally resistant to psychological counseling and investigation, the textual information analysis method is used to identify psychological problems. Based on the text resources generated by the internal student forums of universities, this paper introduces artificial intelligence algorithms and deeply investigates the psychological state evaluation and early warning model
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