An analysis of cognitive change in online mental health communities: A textual data analysis based on post replies of support seekers |
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| Institution: | 1. School of Management, Hefei University of Technology, Hefei 230009, P.R. China;2. Philosophical and Social Laboratory for Data Science and Intelligent Society Governance at Ministry of Education of China, Hefei 230009, P.R. China;3. College of Business, Lamar University, Beaumont, USA;4. Center for Mental Health Education, University of Shanghai for Science and Technology, Shanghai 201210, P.R. China;1. School of Information, Renmin University of China, Beijing 100872, PR China;2. School of Information Technology and Management, University of International Business and Economics, Beijing 100029, PR China;1. School of Information Management, Central China Normal University, Wuhan, 430079, China;3. Center for Studies of Information Resources, Wuhan University, Wuhan, 430072, China;4. Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100020, China;5. School of Information Management, Wuhan University, Wuhan, 430072, China;1. College of Management, Shenzhen University, Shenzhen, China, 518055;2. Greater Bay Area International Institute for Innovations, Shenzhen University, Shenzhen, China, 518055;3. Shenzhen University, Shenzhen, China, 518055;4. School of Logistics, Yunnan University of Finance and Economics, Kunming, 650221, China |
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| 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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