Sentiment and concern evaluation using online health community reviews
In the online health community (OHC), each patient review of doctors includes an evaluation and an emotional attitude toward the doctor. Subsequent patients usually browse the comments of other patients about doctors when choosing a doctor and subsequently make decisions based on these reviews. Through sentiment analysis, a user’s emotional orientation can be judged from the review, enabling an understanding of patients’ emotional tendencies and main concerns regarding doctors during medical treatment. This also provides a reference for OHC doctors to improve service quality. This study used a method based on a sentiment dictionary to analyze the sentiment value of reviews and selected three different types of diseases (diabetes, leukemia, and depression) as examples from user reviews of the “Good Doctor Online” community. SnowNLP, a Python library for Chinese natural language processing, was used to realize the sentiment analysis of the reviews. The program correctly identified the sentiment of most reviews. Although the sentiments of OHC reviews are mostly positive, there are also a few extremely negative reviews. Most positive patient reviews about doctors are related to their good attitude and patience with patients and their condition.
Ahmed, M., Chen, Q., & Li, Z.H. (2020). Constructing domain-dependent sentiment dictionary for sentiment analysis. Neural Computing and Applications, 32(18):14719-14732. https://doi.org/10.1007/s00521-020-04824-8
Bi, Q., Shen, L., Evans, R., Zhang, Z., Wang, S., Dai, W., et al. (2020). Determining the topic evolution and sentiment polarity for albinism in a Chinese online health community: Machine learning and social network analysis. JMIR Medical Informatics, 8(5):e17813. https://doi.org/10.2196/17813
Cai, L., Peng, C., Chen, S., & Guo, L. (2019). Sentiment analysis based on multiple features convolutional neural networks. Computer Engineering, 45(4):169-174, 180. [In Chinese] https://doi.org/10.19678/j.issn.1000-3428.0050338
Chaudhuri, A. (2022). Sentiment Analysis of COVID-19 reviews using hierarchical version of d-RNN. Computacion Y Sistemas, 26(2):1045-1067. https://doi.org/10.13053/CyS-26-2-4143
Chen, X., Shen, Z., Guan, T., Tao, Y., Kang, Y., & Zhang, Y. (2024). Analyzing patient experience on weibo: Machine learning approach to topic modeling and sentiment analysis. JMIR Medical Informatics, 12:e59249. https://doi.org/10.2196/59249
Chen, Z., Song, Q., Wang, A., Xie, D., & Qi, H. (2022). Study on the relationships between doctor characteristics and online consultation volume in the online medical community. Healthcare (Basel), 10(8):1551. https://doi.org/10.3390/healthcare10081551
Cui, J., Wang, Z., Ho, S.B., & Cambria, E. (2023). Survey on sentiment analysis: Evolution of research methods and topics. In: Artificial Intelligence Review. Germany: Springer, p.1-42. https://doi.org/10.1007/s10462-022-10386-z
Elbattah, M., Arnaud, E., Gignon, M., & Dequen, G. (2021). The Role of Text Analytics in Healthcare: A Review of Recent Developments and Applications. In: Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, p.825-832. https://doi.org/10.5220/0010414508250832
Fang, Y., Sun, J., & Han, B. (2020). Research on text sentiment analysis method based on BERT. Information Technology and Informatization, 2:108-111. [In Chinese]
Fu, J., Li, C., Zhou, C., Li, W., Lai, J., Deng, S., et al. (2023). Methods for analyzing the contents of social media for health care: Scoping review. Journal of Medical Internet Research, 25:e43349. https://doi.org/10.2196/43349
Han, H.Y., Zhang, J.P., Yang, J., Shen, Y., & Zhang, Y. (2018). Generate domain-specific sentiment lexicon for review sentiment analysis. Multimedia Tools and Applications, 77(16):21265-2128. https://doi.org/10.1007/s11042-017-5529-5
Ji, S., & Fangbi, T. (2016). Emotion analysis model of MOOC course review based on BiLSTM. International Journal of Emerging Technologies in Learning, 16(8):93-105. https://doi.org/10.3991/ijet.v16i08.18517
Liu, J., & Kong, J. (2021). Why do users of online mental health communities get likes and reposts: A combination of text mining and empirical analysis. Healthcare (Basel), 9(9):1133. https://doi.org/10.3390/healthcare9091133
Liu, J., Kong, J., & Zhang, X. (2020). Study on differences between patients with physiological and psychological diseases in online health communities: Topic analysis and sentiment analysis. International Journal of Environmental Research and Public Health, 17(5):1508. https://doi.org/10.3390/ijerph17051508
Lu, Y., Wu, Y., Liu, J., Li, J., & Zhang, P. (2017). Understanding health care social media use from different stakeholder perspectives: A content analysis of an online health community. Journal of Medical Internet Research, 19(4):e109. https://doi.org/10.2196/jmir.7087
Luo, A., Xin, Z., Yuan, Y., Wen, T., Xie, W., Zhong, Z., et al. (2020). Multidimensional feature classification of the health information needs of patients with hypertension in an online health community through analysis of 1000 patient question records: Observational study. Journal of Medical Internet Research, 22(5):e17349. https://doi.org/10.2196/17349
Nandwani, P., & Verma, R. (2021) A review on sentiment analysis and emotion detection from text. In: Social Network Analysis and Mining. Vol. 11. Germany: Springer, p.81. https://doi.org/10.1007/s13278-021-00776-6
Necaise, A., & Amon, M.J. (2024). Peer support for chronic pain in online health communities: Quantitative study on the dynamics of social interactions in a chronic pain forum. Journal of Medical Internet Research, 26:e45858. https://doi.org/10.2196/45858
Ortigosa, A., Martín, José M., & Carro, R.M. (2014). Sentiment analysis in facebook and its application to e-learning. Computers in Human Behavior, 31:527-541. https://doi.org/10.1016/j.chb.2013.05.024
Pan, X., Tang, Z., Liu, Y., & Ren, J. (2024). Analysis on childhood fever health information seeking behaviors in online health community using a mixed-methods approach. Digital Health, 10:1-15. https://doi.org/10.1177/20552076241282622
Rustam, F., Khalid, M., Aslam, W., Rupapara, V., Mehmood, A., & Choi, G.S. (2021). A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis. PLos One, 16(2):e0245909. https://doi.org/10.1371/journal.pone.0245909
Shen, L., Yao, R., Zhang, W., Evans, R., Cao, G., & Zhang, Z. (2021). Emotional attitudes of chinese citizens on social distancing during the COVID-19 outbreak: Analysis of social media data. JMIR Medical Informatics, 9(3):e27079. https://doi.org/10.2196/27079
SnowNLP. (2017). SnowNLP: Simplified Chinese Text Processing. https://github.com/isnowfy/snownlp [Last accessed on 2025 Jan 05].
Sun, Y., Yu, J., Chiu, Y.L., & Hsu, Y.T. (2022). Can online health information sources really improve patient satisfaction? Frontiers in Public Health, 10:940800. https://doi.org/10.3389/fpubh.2022.940800
Wu, J., Hou, S.X., Jin, M.M., & Hou, Z.Y. (2017). LDA feature selection based text classification and user clustering in Chinese online health community. Journal of China Society for Scientific and Technical Information, 36(11):1183-1191.
Xiang, M., Zhong, D., Han, M., & Lv, K. (2023). A study on online health community users’ information demands based on the BERT-LDA model. Healthcare (Basel), 11(15):2142. https://doi.org/10.3390/healthcare11152142
Zeng, J.F., Ma, X., & Zhou, K. (2019). Enhancing attention-based LSTM with position context for aspect-level sentiment classification. IEEE Access, 7:20462-20471. https://doi.org/10.1109/ACCESS.2019.2893806
Zhang, S., Bantum, E., Owen, J., & Elhadad, N. (2014). Does sustained participation in an online health community affect sentiment? AMIA Symposium, 2014:1970-1979.