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Research and Analysis on Psychological Health Problems of College Students Based on Improved BiLSTM Algorithm(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2023年04期
Page:
43-49
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Research and Analysis on Psychological Health Problems of College Students Based on Improved BiLSTM Algorithm
Author(s):
Gao Xingyu1; 2; Shi Jiaojie1; 2; Chen Jian3
(1.Zhejiang Provincial Institute of Culture and Tourism Development,Hangzhou 311231,China)
(2.School of Hotel Management,Zhejiang Tourism Vocational College,Hangzhou 311231,China)
(3.Zhejiang University of Technology,Hangzhou 310023,China)
Keywords:
college student psychology; sentiment analysis; deep learning; BiLSTM; word vector
PACS:
TP391
DOI:
10.3969/j.issn.1672-1292.2023.04.006
Abstract:
With the more and more wide application of deep learning models,the accuracy of the models continues to improve,providing feasibility for intelligent research and judgment systems. The psychological behavior of college students have both explicitness and implicitness. At present,implicit information is often overlooked in the process of psychological counseling. In order to extract implicit information more effectively,this paper uses the deep learning method to extract the psychological characteristics of college students' psychological interview data,and constructs an intelligent analysis algorithm for college students' psychological counseling data. In order to deepen the emotional orientation in the word vector,this paper uses the BERT model to replace the traditional Word2vec model. And the BiLSTM algorithm is used to strengthen the correlation between contexts. Experiments prove that the algorithm effectively obtains metaphorical and low-frequency semantic information in the process of psychological counseling,classifies psychological counseling data(positive emotion and negative emotion),and accurately warns the interview data of negative emotions.

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Last Update: 2023-12-15