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research-article

An analysis of cognitive change in online mental health communities: : A textual data analysis based on post replies of support seekers

Authors:

Changyong Liang,

Hu LiuAuthors Info & Claims

Volume 60, Issue 2

https://doi.org/10.1016/j.ipm.2022.103192

Published: 01 March 2023 Publication History

Highlights

•

We propose a novel model for cognitive change recognition from reply posts.

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We propose a Chinese dataset for cognitive change recognition.

•

We consider both text information and graphic emotional information.

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The proposed method outperforms the baselines and the dictionary-based method.

Abstract

The replies of people seeking support in online mental health communities can be analyzed to discover if they feel better after receiving support; feeling better indicates a cognitive change. Most research uses key phrase matching and word frequency statistics to identify psychological cognitive change, methods that result in omissions and inaccuracy. This study constructs an intelligent method for identifying psychological cognitive change based on natural language processing technology. It incorporates information related to emotions that appears in reply text to help identify whether psychological cognitive change has occurred. The model first encodes the emotion information based on rule matching and manual annotation, then adds the encoded emotion lexicon and a cognitive change lexicon to a word2vec high-dimensional semantic word vector training, converts the annotated cognitive change recognition text into a vector matrix using the trained model, and train in the annotated text using TextCNN. To compare the results with those of the traditional methods (key phrase matching and sentiment word frequency statistics), this study uses a semi-automated approach to construct a lexicon of psychological cognitive change, as well as a keyword lexicon without cognitive change, based on word vectors and similarity. We compare the performance of the classifier before and after the fusion of the graphical emotion information, compare the LSTM and Transformer as baselines, and compare traditional word frequency statistics methods. The experimental results show that our proposed classification model performs better than the others; it achieves 84.38% precision, an 84.09% recall rate, and an 84.17% F1 value. Our work bears methodological implications for online mental health platforms.

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Index Terms

An analysis of cognitive change in online mental health communities: A textual data analysis based on post replies of support seekers

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

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Published In

cover image Information Processing and Management: an International Journal

Information Processing and Management: an International Journal Volume 60, Issue 2

Mar 2023

1443 pages

ISSN:0306-4573

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Copyright © 2022.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 March 2023

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Cited By

Jiang LYang LZang XDong JLu W(2024)Cooperated delivery of drones and truck for “last 100 metres” in rural areasJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23304546:1(2595-2614)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JIFS-233045
Zhao WGu DYang XJia MLiang CWang XZolotarev O(2024)MedT2TFuture Generation Computer Systems10.1016/j.future.2024.07.030161:C(586-600)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.future.2024.07.030
Gu DLiu HZhao HYang XLi MLiang C(2024)A deep learning and clustering‐based topic consistency modeling framework for matching health information supply and demandJournal of the Association for Information Science and Technology10.1002/asi.2484675:2(152-166)Online publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1002/asi.24846
Ma JWang LFu ZShao HGuo W(2023)Capturing mental modelsAdvanced Engineering Informatics10.1016/j.aei.2023.10208357:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.aei.2023.102083

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