Psychological Education Health Assessment Problems Based on Improved Constructive Neural Network

Yang Li; Jia Ze Li; Qi Fan; Xin Li; Zhihong Wang

doi:10.3389/fpsyg.2022.943146

Psychological Education Health Assessment Problems Based on Improved Constructive Neural Network

Front Psychol. 2022 Aug 2:13:943146. doi: 10.3389/fpsyg.2022.943146. eCollection 2022.

Authors

Yang Li¹, Jia Ze Li², Qi Fan³, Xin Li¹, Zhihong Wang¹

Affiliations

¹ School of Administration, Nanjing Forest Police College, Nanjing, China.
² School of Foreign Studies, Nanjing University, Nanjing, China.
³ Institute of Mental Health, Nanjing Xiaozhuang University, Nanjing, China.

Abstract

In order to better assess the mental health status, combining online text data and considering the problems of lexicon sparsity and small lexicon size in feature statistics of word frequency of the traditional linguistic inquiry and word count (LIWC) dictionary, and combining the advantages of constructive neural network (CNN) convolutional neural network in contextual semantic extraction, a CNN-based mental health assessment method is proposed and evaluated with the measurement indicators in CLPsych2017. The results showed that the results obtained from the mental health assessment by CNN were superior in all indicators, in which F1 = 0.51 and ACC = 0.69. Meanwhile, ACC evaluated by FastText, CNN, and CNN + Word2Vec were 0.66, 0.67, 0.67, and F1 were 0.37, 0.47, and 0.49, respectively, which indicates the use of CNN in mental health assessment has feasibility.

Keywords: CNN; LIWC dictionary; assessment; mental health assessment; psychological.