Open AccessDepression Tendency Screening Use Text Based Emotional Analysis Technique
Author(s) -
Chujun Yang,
Xiaoxiong Lai,
Zhe Hu,
Yanni Liu,
Peng Shen
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1237/3/032035
Subject(s) - depression (economics) , support vector machine , computer science , convolutional neural network , artificial intelligence , psychology , machine learning , natural language processing , economics , macroeconomics
This paper proposes a text recognition model for semantic analysis of the interview records related to depression, which can effectively identify whether the interviewee is a patient with depression tendency. It mainly consists of two components: 1) The framework of Support Vector Machine (SVM) for Classification of depression related questions; 2) the framework of Doc2vec and Text Convolutional Neural Network (TextCNN) for classification of whether the interviewee has a tendency to depression. Finally, the results obtained by the two classification methods are combined to establish a text classification model that is easy to analyze the tendency of depression.