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| Examining Topics and Sentiments of Chronic Disease Patients’ Online Reviews — Case Study of “Sweet Homeland” |
Yu Jiaqi1,2,Zhao Doudou1,Liu Rui1( ) |
1School of Information Management, Central China Normal University, Wuhan 430079, China
2Hubei Normal University Library, Huangshi 435002, China |
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Abstract [Objective] This paper constructs a model for topic-sentiment collaborative mining, aiming to understand chronic disease patients at different stages better. [Methods] First, we added sentiment and time features to the LDA model to create the new dUTSU (dynamic unsupervised topic and sentiment unification) model. Then, we retrieved posts by diabetes patients from an online health community. Finally, we assessed the dUTSU model’s performance with the topic-sentiment analysis and the topic-sentiment evolution analysis. [Results] The dUTSU model had better perplexity, average topic similarity, and sentiment classification accuracy than the JST, ASUM, and UTSU models. The model identified 15 topics and captured trending topics, sentiment, and intensity across seven distinct periods, including the disease diagnosis stage and the complication stage. The model also revealed the topic-sentiment evolution over time. [Limitations] The experiment only used the diabetics reviews. We did not consider patients’ geographical locations, personal attributes, and social relationships. [Conclusions] The dUTSU model could effectively extract topic-sentiment data collaboratively reviews from patients with chronic diseases. The findings can serve as valuable references for online health communities, medical institutions, and patients to carry out health services.
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Received: 24 August 2022
Published: 28 March 2023
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| Fund:National Social Science Fund of China(22&ZD324) |
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Corresponding Authors:
Liu Rui，ORCID：0000-0002-5450-4947，E-mail：liuruiccnu@hotmail.com。
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