[1]高星宇,施姣杰,陈 坚.基于改进BiLSTM算法的大学生心理健康问题研究分析[J].南京师范大学学报(工程技术版),2023,23(04):043-49.[doi:10.3969/j.issn.1672-1292.2023.04.006]
　Gao Xingyu,Shi Jiaojie,Chen Jian.Research and Analysis on Psychological Health Problems of College Students Based on Improved BiLSTM Algorithm[J].Journal of Nanjing Normal University(Engineering and Technology),2023,23(04):043-49.[doi:10.3969/j.issn.1672-1292.2023.04.006]
点击复制

基于改进BiLSTM算法的大学生心理健康问题研究分析
分享到：

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

卷:
23卷
期数:
2023年04期
页码:
043-49
栏目:
计算机科学与技术
出版日期:
2023-12-15

文章信息/Info

Title:
Research and Analysis on Psychological Health Problems of College Students Based on Improved BiLSTM Algorithm
文章编号:
1672-1292(2023)04-0043-07
作者:
高星宇1; 2; 施姣杰1; 2; 陈 坚3
(1.浙江省文化和旅游发展研究院,浙江 杭州 311231)
(2.浙江旅游职业学院酒店管理学院,浙江 杭州 311231)
(3.浙江工业大学,浙江 杭州 310023)
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)
关键词:
大学生心理; 情感分析; 深度学习; BiLSTM; 词向量
Keywords:
college student psychology; sentiment analysis; deep learning; BiLSTM; word vector
分类号:
TP391
DOI:
10.3969/j.issn.1672-1292.2023.04.006
文献标志码:
A
摘要:
随着深度学习模型应用越来越广泛,模型精度不断提高,为智能化研判系统提供了可行性. 大学生的心理行为同时具备外显性和内隐性,目前在心理咨询过程中内隐性信息往往容易被忽视. 为更有效提取内隐信息,通过深度学习方法对大学生心理访谈数据进行心理特征提取,构建大学生心理咨询智能化分析算法. 为加深词向量中的情感导向,采用BERT模型替换传统的Word2vec模型,并采用双向LSTM算法加强上下文之间的关联性. 实验证明,该算法可有效获取心理咨询过程中隐喻、低频的语义信息,对心理咨询数据进行二分类,并准确对负面情绪的访谈数据进行预警.
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.

参考文献/References:

[1]俞国良,王浩. 构建新时代心理健康教育新格局[N]. 中国社会科学报,2021-12-09(007).
[2]黄潇潇,张亚利,俞国良. 2010～2020中国内地大学生心理健康问题检出率的元分析[J]. 心理科学进展,2022,30(5):991-1004.
[3]赵志川. 大学生心理咨询与心理健康教育概述——评《大学生心理咨询与心理健康教育》[J]. 中国教育学刊,2023,362(6):127.
[4]陈爱平. 高职院校学生心理健康教育现状与实施策略探究[J]. 创新创业理论研究与实践,2021,4(9):58-59.
[5]刘潇,张磊. 高校辅导员谈心谈话工作开展探究[J]. 教育理论与实践,2020,40(6):41-43.
[6]原多多. 产品评论文本的情感分析方法研究[D]. 兰州:兰州财经大学,2019.
[7]姚天昉,程希文,徐飞玉,等. 文本意见挖掘综述[J]. 中文信息学报,2008,22(3):71-80.
[8]钟丁媛,高峥洲,金皓辰,等. 基于NLP文字处理的评论有用性探究[J]. 科技风,2020(31):150-153.
[9]陈颖,肖仲喆. 离散标签与维度空间结合的语音数据库设计[J]. 声学技术,2018,37(4):380-387.
[10]WIEBE J M,BRUCE R F,O'HARA T P. Development and use of a gold-standard data set for subjectivity classifications[C]//Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics. Stroudsburg,USA:ACL,1999:246-253.
[11]KAMAL A. Subjectivity classification using machine learning techniques for mining feature-opinion pairs from web opinion sources[J/OL]. arXiv Preprint arXiv:1312.6962,2013.
[12]SCHOUTEN K,FRASINCAR F,DE JONG F. Commit-P1wp3:a co-occurrence based approach to aspect-level sentiment analysis[C]//Proceedings of the 8th International Workshop on Semantic Evaluation(SemEval 2014).Dublin,Ireland:ACL,2014.
[13]肖海艳. 基于深度学习的在线教师课程评论情感分析模型[J]. 电子设计工程,2023,31(6):39-42.
[14]WANI T M,GUNAWAN T S,QADRI S A A,et al. A comprehensive review of speech emotion recognition systems[J]. IEEE Access,2021,9:47795-47814.
[15]柴源. 基于LSTM和Word2vec的图书评论文本情感分析研究[J]. 信息技术,2022,46(7):59-64.
[16]MIKOLOV T,CHEN K,CORRADO G,et al. Efficient estimation of word representations in vector space[J/OL]. arXiv Preprint arXiv:1301.3781,2013.
[17]谢思雅,施一萍,胡佳玲,等. 基于BiLSTM-ATT的微博用户情感分类研究[J]. 传感器与微系统,2021,40(2):26-29.
[18]DEVLIN J,CHANG M W,LEE K,et al. BERT:pre-training of deep bidirectional transformers for language understanding[J/OL]. arXiv Preprint arXiv:1810.04805,2018.
[19]ZHANG C,LI Q C,SONG D W. Syntax-aware aspect-level sentiment classification with proximity-weighted convolution network[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in information Retrieval. New York,USA:ACM,2019:1145-1148.
[20]CORCORAN C M,CARRILLO F,FERNÁNDEZ-SLEZAK D,et al. Prediction of psychosis across protocols and risk cohorts using automated language analysis[J]. World Psychiatry:Official Journal of the World Psychiatric Association(WPA),2018,17(1):67-75.
[21]STARLET B A,BEN B P,LEENA M. Utterance and syllable level prosodic features for automatic emotion recognition[C]//Proceedings of the 2018 IEEE Recent Advances in Intelligent Computational Systems(RAICS). Thiruvananthapuram,India:IEEE,2018:31-35.
[22]TADESSE M M,LIN H F,XU B,et al. Detection of suicide ideation in social media forums using deep learning[J]. Algorithms,2019,13(1):7.
[23]GREFF K,SRIVASTAVA R K,KOUTNÍK J,et al. LSTM:a search space odyssey[J]. IEEE Transactions on Neural Networks and Learning Systems,2016,28(10):2222-2232.

相似文献/References:

[1]汤丽华,吴星宇,徐华健,等.公路交通舆情监测及系统开发[J].南京师范大学学报(工程技术版),2021,21(04):033.[doi:10.3969/j.issn.1672-1292.2021.04.006]
　Tang Lihua,Wu Xingyu,Xu Huajian,et al.Monitor and System Development of Road Transportation Public Opinion[J].Journal of Nanjing Normal University(Engineering and Technology),2021,21(04):033.[doi:10.3969/j.issn.1672-1292.2021.04.006]

备注/Memo

备注/Memo:
收稿日期:2023-04-28.
基金项目:浙江旅游职业学院招标课题专项项目(2023ZB02).
通讯作者:高星宇,助教,研究方向:深度学习. E-mail:gaoxingyu@tourzj.edu.cn
更新日期/Last Update: 2023-12-15