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Analyzing user-generated content using natural language processing: a case study of public satisfaction with healthcare systems

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  • Anna Ruelens

    (University of Leuven)

Abstract

While user-generated online content (UGC) is increasingly available, public opinion studies are yet to fully exploit the abundance and richness of online data. This study contributes to the practical knowledge of user-generated online content and machine learning techniques that can be used for the analysis of UGC. For this purpose, we explore the potential of user-generated content and present an application of natural language pre-processing, text mining and sentiment analysis to the question of public satisfaction with healthcare systems. Concretely, we analyze 634 online comments reflecting attitudes towards healthcare services in different countries. Our analysis identifies the frequency of topics related to healthcare services in textual content of the comments and attempts to classify and rank national healthcare systems based on the respondents’ sentiment scores. In this paper, we describe our approach, summarize our main findings, and compare them with the results from cross-national surveys. Finally, we outline the typical limitations inherent in the analysis of user-generated online content and suggest avenues for future research.

Suggested Citation

  • Anna Ruelens, 2022. "Analyzing user-generated content using natural language processing: a case study of public satisfaction with healthcare systems," Journal of Computational Social Science, Springer, vol. 5(1), pages 731-749, May.
    • Handle: RePEc:spr:jcsosc:v:5:y:2022:i:1:d:10.1007_s42001-021-00148-2
    DOI: 10.1007/s42001-021-00148-2
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    1. Bennett Kleinberg & Isabelle Vegt & Paul Gill, 2021. "The temporal evolution of a far-right forum," Journal of Computational Social Science, Springer, vol. 4(1), pages 1-23, May.
    2. Isabelle Vegt & Maximilian Mozes & Paul Gill & Bennett Kleinberg, 2021. "Correction to: Online influence, offline violence: language use on YouTube surrounding the ‘Unite the Right’ rally," Journal of Computational Social Science, Springer, vol. 4(2), pages 921-923, November.
    3. Isabelle Vegt & Maximilian Mozes & Paul Gill & Bennett Kleinberg, 2021. "Online influence, offline violence: language use on YouTube surrounding the ‘Unite the Right’ rally," Journal of Computational Social Science, Springer, vol. 4(1), pages 333-354, May.
    4. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    5. Bilal Naeem & Aymen Khan & Mirza Omer Beg & Hasan Mujtaba, 2020. "A deep learning framework for clickbait detection on social area network using natural language cues," Journal of Computational Social Science, Springer, vol. 3(1), pages 231-243, April.
    6. Shadi Shahsavari & Pavan Holur & Tianyi Wang & Timothy R. Tangherlini & Vwani Roychowdhury, 2020. "Conspiracy in the time of corona: automatic detection of emerging COVID-19 conspiracy theories in social media and the news," Journal of Computational Social Science, Springer, vol. 3(2), pages 279-317, November.
    7. C. A. Piña-García & J. Mario Siqueiros-García & E. Robles-Belmont & Gustavo Carreón & Carlos Gershenson & Julio Amador Díaz López, 2018. "From neuroscience to computer science: a topical approach on Twitter," Journal of Computational Social Science, Springer, vol. 1(1), pages 187-208, January.
    8. Adam Badawy & Emilio Ferrara, 2018. "The rise of Jihadist propaganda on social networks," Journal of Computational Social Science, Springer, vol. 1(2), pages 453-470, September.
    9. Austin Horng-En Wang & Mei-chun Lee & Min-Hsuan Wu & Puma Shen, 2020. "Influencing overseas Chinese by tweets: text-images as the key tactic of Chinese propaganda," Journal of Computational Social Science, Springer, vol. 3(2), pages 469-486, November.
    10. Joshua Uyheng & Kathleen M. Carley, 2020. "Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines," Journal of Computational Social Science, Springer, vol. 3(2), pages 445-468, November.
    11. Wataru Souma & Irena Vodenska & Hideaki Aoyama, 2019. "Enhanced news sentiment analysis using deep learning methods," Journal of Computational Social Science, Springer, vol. 2(1), pages 33-46, January.
    12. Elias Mossialos, 1997. "Citizens' Views on Health Care Systems in the 15 Member States of The European Union," Health Economics, John Wiley & Sons, Ltd., vol. 6(2), pages 109-116, March.
    13. Cohen, G., 1996. "Age and health status in a patient satisfaction survey," Social Science & Medicine, Elsevier, vol. 42(7), pages 1085-1093, April.
    14. Nicholas Francis Havey, 2020. "Partisan public health: how does political ideology influence support for COVID-19 related misinformation?," Journal of Computational Social Science, Springer, vol. 3(2), pages 319-342, November.
    15. Jensen, Carsten & Naumann, Elias, 2016. "Increasing pressures and support for public healthcare in Europe," Health Policy, Elsevier, vol. 120(6), pages 698-705.
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