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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Hybrid Topic Cluster Models for Social Healthcare Data

Author 1: K Rajendra Prasad
Author 2: Moulana Mohammed
Author 3: R M Noorullah

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2019.0101168

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 11, 2019.

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Abstract: Social media and in particular, microblogs are becoming an important data source for disease surveillance, behavioral medicine, and public healthcare. Topic Models are widely used in microblog analytics for analyzing and integrating the textual data within a corpus. This paper uses health tweets as microblogs and attempts the health data clustering by topic models. The traditional topic models, such as Latent Semantic Indexing (LSI), Probabilistic Latent Schematic Indexing (PLSI), Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), and integer Joint NMF(intJNMF) methods are used for health data clustering; however, they are intractable to assess the number of health topic clusters. Proper visualizations are essential to extract the information from and identifying trends of data, as they may include thousands of documents and millions of words. For visualization of topic clouds and health tendency in the document collection, we present hybrid topic models by integrating traditional topic models with VAT. Proposed hybrid topic models viz., Visual Non-negative Matrix Factorization (VNMF), Visual Latent Dirichlet Allocation (VLDA), Visual Probabilistic Latent Schematic Indexing (VPLSI) and Visual Latent Schematic Indexing (VLSI) are promising methods for accessing the health tendency and visualization of topic clusters from benchmarked and Twitter datasets. Evaluation and comparison of hybrid topic models are presented in the experimental section for demonstrating the efficiency with different distance measures, include, Euclidean distance, cosine distance, and multi-viewpoint cosine similarity.

Keywords: Multi-viewpoint based metric; traditional topic models; hybrid topic models; topic visualization; health tendency

K Rajendra Prasad, Moulana Mohammed and R M Noorullah, “Hybrid Topic Cluster Models for Social Healthcare Data” International Journal of Advanced Computer Science and Applications(IJACSA), 10(11), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101168

@article{Prasad2019,
title = {Hybrid Topic Cluster Models for Social Healthcare Data},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0101168},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101168},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {11},
author = {K Rajendra Prasad and Moulana Mohammed and R M Noorullah}
}


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