Abstract
In the information age, people are spending increasing time and energy to search and involve in health topics. And processes in social network offer new ways of investigating public opinion on this topic. However, when we are talking about medical service, what do we concern? The deterioration of doctor-patient relationship? The medical insurance policy? Or the medical achievements we have got? Understanding hot topics of medical issues and its dynamic changes help us to guide a healthy doctor-patient relationship and maintain a stable online public opinion environment. In order to figure out the question, we collected the Weibo tweets about medical information and then extracted the subjects by LDA model. Due to the feature sparsity and semantic fuzziness of short texts, this paper extended the features by using Word2vec. Finally, we summarized 14 hot subjects of medical service and analyzed the dynamic change of subjects’ frequency and emotion. We find that most of the subjects are related to the medical system reform and the doctor-patient relationship. What’s more, public’s attitude to medical issues is gradually becoming growing positive.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Brown, P.: Naming and framing: the social construction of diagnosis and illness. J. Health Soc. Behav. 35(1), 35–42 (1995)
Finfgeld-Connett, D.: Twitter and health science research. West J. Nurs. Res. 37(10), 1269 (2014)
Signorini, A., Segre, A.M., Polgreen, P.M.: The use of twitter to track levels of disease activity and public concern in the U.S. during the influenza a H1N1 pandemic. Plos One 6(5), e19467 (2011)
Charlessmith, L.E., Reynolds, T.L., Cameron, M.A., et al.: Using social media for actionable disease surveillance and outbreak management: a systematic literature review. Plos One 10(10), e139701 (2015)
Lister, C., Royne, M., Payne, H.E., et al.: The laugh model: reframing and rebranding public health through social media. Am. J. Public Health 105(11), 2245 (2015)
Woo, H., Cho, Y., Shim, E., et al.: Public trauma after the sewol ferry disaster: the role of social media in understanding the public mood. Int. J. Environ. Res. Public Health 12(9), 10974–10983 (2015)
Hays, R., Dakerwhite, G.: The care data consensus? a qualitative analysis of opinions expressed on twitter. BMC Public Health 15(1), 838 (2015)
Balahur, A., Mihalcea, R., Montoyo, A.: Computational approaches to subjectivity and sentiment analysis: present and envisaged methods and applications. Comput. Speech Lang. 28(1), 1–6 (2014)
Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, San Rafael (2012)
Ji, X., Chun, S.A., Wei, Z., et al.: Twitter sentiment classification for measuring public health concerns. Soc. Netw. Anal. Min. 5(1), 13 (2015)
Choudhury, M.D., Counts, S., Horvitz, E.: Major life changes and behavioral markers in social media: case of childbirth. In: Conference on Computer Supported Cooperative Work, pp. 1431–1442. ACM (2013)
Ybarra, M.L.: Help seeking behavior and the internet: a national survey. Int. J. Med. Informatics 75(1), 29–41 (2006)
Rice, R.E.: Influences, usage, and outcomes of internet health information searching: multivariate results from the pew surveys. Int. J. Med. Informatics 75(1), 8–28 (2006)
Kagashe, I., Yan, Z., Suheryani, I.: Enhancing seasonal influenza surveillance: topic analysis of widely used medicinal drugs using twitter data. J. Med. Internet Res. 19(9), e315 (2017)
Didi, S., Quoc, N.D., Georgina, K., et al.: Characterizing twitter discussions about HPV vaccines using topic modeling and community detection. J. Med. Internet Res. 18(8), e232 (2016)
Greaves, F., Ramirezcano, D., Millett, C., et al.: Use of sentiment analysis for capturing patient experience from free-text comments posted online. J. Med. Internet Res. 15(11), e239 (2013)
Metwally, O., Blumberg, S., Ladabaum, U., et al.: Using social media to characterize public sentiment toward medical interventions commonly used for cancer screening: an observational study. J. Med. Internet Res. 19(6), e200 (2017)
Liu, Y., Zhu, X.: Short text sentiment classification based on feature extension and ensemble classifier. In: International Conference on Computer-Aided Design, Manufacturing Modeling and Simulation, pp. 020051 (2018)
Ji, Y.L., Dernoncourt, F.: Sequential Short-Text Classification With Recurrent and Convolutional Neural Networks, pp. 515–520 (2016)
Man, Y.: Feature extension for short text categorization using frequent term sets. Procedia Comput. Sci. 31, 663–670 (2014)
Liu, W., Cao, Z., Wang, J., et al.: Short text classification based on Wikipedia and Word2vec. In: IEEE International Conference on Computer and Communications, pp. 1195–1200. IEEE (2017)
Zhang, L., Li, J., Wang, C.: Automatic synonym extraction using Word2Vec and spectral clustering. In: Chinese Control Conference, pp. 5629–5632 (2017)
Yu, L.C., Wang, J., Lai, K.R., et al.: Refining word embeddings using intensity scores for sentiment analysis. IEEE/ACM Trans. Audio Speech Lang. Process. 26(3), 671–681 (2018)
Kusner, M.J., Sun, Y., Kolkin, N.I., et al.: From word embeddings to document distances. In: International Conference on International Conference on Machine Learning, JMLR.org, pp. 957–966 (2015)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(2605), 2579–2605 (2008)
Chen, Z., Barros, C.P., Hou, X.: Has the medical reform improved the cost efficiency of Chinese hospitals? Soc. Sci. J. 53(4), 510–520 (2016)
Jacobs, L.R., Mettler, S.: Why public opinion changes: the implications for health and health policy. J. Health Polit. Policy Law 36(6), 917–933 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, K., He, C., Wang, L., Wu, J. (2018). When We Talk About Medical Service, What Do We Concern? A Text Analysis of Weibo Data. In: Chen, J., Yamada, Y., Ryoke, M., Tang, X. (eds) Knowledge and Systems Sciences. KSS 2018. Communications in Computer and Information Science, vol 949. Springer, Singapore. https://doi.org/10.1007/978-981-13-3149-7_4
Download citation
DOI: https://doi.org/10.1007/978-981-13-3149-7_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3148-0
Online ISBN: 978-981-13-3149-7
eBook Packages: Computer ScienceComputer Science (R0)