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.
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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
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DOI: https://doi.org/10.1007/978-3-031-00828-3_14
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