文章基本信息

标题：Hybrid Topic Cluster Models for Social Healthcare Data
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作者：K Rajendra Prasad ; Moulana Mohammed ; R M Noorullah 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：11
DOI：10.14569/IJACSA.2019.0101168
出版社：Science and Information Society (SAI)
摘要：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.
关键词：Multi-viewpoint based metric; traditional topic models; hybrid topic models; topic visualization; health tendency