Li Yang, Li Jia Ze, Fan Qi, Li Xin, Wang Zhihong
School of Administration, Nanjing Forest Police College, Nanjing, China.
School of Foreign Studies, Nanjing University, Nanjing, China.
Front Psychol. 2022 Aug 2;13:943146. doi: 10.3389/fpsyg.2022.943146. eCollection 2022.
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.
为了更好地评估心理健康状况，结合在线文本数据，考虑到传统语言查询与词频统计（LIWC）词典在特征统计中存在的词汇稀疏和词典规模小的问题，并结合卷积神经网络（CNN）在上下文语义提取方面的优势，提出了一种基于CNN的心理健康评估方法，并使用CLPsych2017中的测量指标进行评估。结果表明，通过CNN进行心理健康评估所获得的结果在所有指标上均更优，其中F1 = 0.51，ACC = 0.69。同时，由FastText、CNN以及CNN + Word2Vec评估得到的ACC分别为0.66、0.67、0.67，F1分别为0.37、0.47、0.49，这表明CNN在心理健康评估中的应用具有可行性。
