Skip to main content

LDA Based Topic Modeling on Hospital Facebook Posts

  • 64 Accesses

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 457)

Abstract

Topic modeling is a popular method used to discover latent topics hidden in text corpora. Applied to social media, it offers insights into understanding the contents of social media data. This study aims to model the topics found in two hospitals’ Facebook pages, particularly we used the Latent Dirichlet Allocation (LDA) technique to extract 20 topics from the Facebook posts of two hospitals representing one rural and one urban hospital. The results revealed five topics that are prevalent in the urban hospital and one topic in the rural hospital. The finding also disclosed an interesting overall trend among the topics that are posted by the urban and rural hospitals. Hospital’s Facebook platform can provide valuable information regarding the current state of affairs in health care institutions. Comparison of this information can help the stakeholders to plan better information dissemination programs.

Keywords

  • Topic modelling
  • Latent Dirichlet allocation
  • Hospital Facebook posts
  • Unsupervised machine learning

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-031-00828-3_14
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   229.00
Price excludes VAT (USA)
  • ISBN: 978-3-031-00828-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   299.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.

References

  1. Koltsova, O., Koltcov, S.: Mapping the public agenda with topic modeling: the case of the Russian livejournal. Policy Internet 5(2), 207–227 (2013)

    CrossRef  Google Scholar 

  2. DePaula, N., Harrison, T.: The EPA under the Obama and Trump administrations: using LDA topic modeling to discover themes, issues and policy agendas on Twitter. In: The Internet, Policy & Politics Conference. Oxford Internet Institute (2018)

    Google Scholar 

  3. Sridhar, V.K.: Unsupervised topic modeling for short texts using distributed representations of words. In: Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing, pp. 192–200 (2015)

    Google Scholar 

  4. Li, X., Verspoor, K., Gray, K., Barnett, S.: Discovery of learning topics in an online social network for health professionals. In: CEUR Workshop Proceedings, pp. 1–6 (2016)

    Google Scholar 

  5. Nzali, M.D., Bringay, S., Lavergne, C., Mollevi, C., Opitz, T.: What patients can tell us: topic analysis for social media on breast cancer. JMIR Med. Inform. 5(3), e23 (2017)

    Google Scholar 

  6. Noteboom, C., Al-Ramahi, M.: What are the gaps in mobile patient portal? Mining users feedback using topic modeling. In: Proceedings of the 51st Hawaii International Conference on System Sciences, pp. 564–573 (2018)

    Google Scholar 

  7. Asghari, M., Sierra-Sosa, D., Elmaghraby, A.: Trends on health in social media: analysis using twitter topic modeling. In: 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 558–563 (2018)

    Google Scholar 

  8. Nakayama, J.Y., Hertzberg, V., Ho, J.C.: Making sense of abbreviations in nursing notes: a case study on mortality prediction. In: AMIA Summits on Translational Science Proceedings, pp. 275–284 (2019)

    Google Scholar 

  9. Tang, C., Zhou, L., Plasek, J., Rozenblum, R., Bates, D.: Comment topic evolution on a cancer institution’s Facebook page. Appl. Clin. Inform. 8(03), 854–865 (2017)

    CrossRef  Google Scholar 

  10. Blei, D.M.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012)

    CrossRef  Google Scholar 

  11. Sharma, D., Kumar, B., Chand, S.: A survey on journey of topic modeling techniques from SVD to deep learning. Int. J. Mod. Educ. Comput. Sci. 9(7), 50–62 (2017)

    CrossRef  Google Scholar 

  12. Chauhan, U., Shah, A.: Topic modeling using latent Dirichlet allocation: a survey. ACM Comput. Surv. (CSUR) 54(7), 1–35 (2021)

    CrossRef  Google Scholar 

  13. Rahim, A.I., Ibrahim, M.I., Musa, K.I., Chua, S.L.: Facebook reviews as a supplemental tool for hospital patient satisfaction and its relationship with hospital accreditation in Malaysia. Int. J. Environ. Res. Public Health 18(14), 7454 (2021)

    CrossRef  Google Scholar 

  14. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 1(3), 993–1022 (2003)

    MATH  Google Scholar 

  15. Tresnasari, N.A., Adji, T.B., Permanasari, A.E.: Social-child-case document clustering based on topic modeling using latent Dirichlet allocation. Indon. J. Comput. Cybern. Syst. 14(2), 179–188 (2020)

    CrossRef  Google Scholar 

  16. Gui, L., Leng, J., Pergola, G., Zhou, Y., Xu, R., He, Y.: Neural topic model with reinforcement learning. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3478–3483 (2019)

    Google Scholar 

  17. Pathan, A.F., Prakash, C.: Unsupervised aspect extraction algorithm for opinion mining using topic modeling. Glob. Transit. Proc. 2, 492–499 (2021)

    CrossRef  Google Scholar 

  18. Rahman, M., Frame, J., Lin, J., Nearing, G.: Hidden stories: topic modeling in hydrology literature (2020). https://doi.org/10.31223/osf.io/2sy7a

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Siti Sakira Kamaruddin .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Kamaruddin, S.S., Ahmad, F.K., Taiye, M.A. (2022). LDA Based Topic Modeling on Hospital Facebook Posts. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_14

Download citation