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• W3185793131 abstract "AbstractSocial media is the leading platform to accomplish large data in the field of health discipline tweets everywhere in the world empowerment currently. And it is a prominent data source for searching health terms (topics) and predicts solutions in the direction of health care. Health care has become one of the largest sectors in the world in terms of income and employment. Billions of customers use Twitter daily to enable people to share health-related topics of their views and opinions on various healthcare topics. Topic models commence from natural language processing (NLP) to acquiring immeasurable knowledge on healthcare areas which highly motivated to analyze the topic models (TM). Topic models are addressed intended for the squeezing of health topics for modeling the selective latent tweet documents in the healthcare system. Analyzing the topics in TM is an essential issue and facilitates an unreliable number of topics in TM that address the destitute results in health-related clustering (HRC) in various structured and unstructured data. In this regard, needful visualizations are imperative measurements to clipping the information for identifying cluster direction. So that, to believe and contribute proposed distributed multimodal active topic models such as Hadoop distributed non-negative matrix factorization (HdinNMF), Hadoop distributed latent Dirichlet allocation (HdiLDA), and Hadoop distributed probabilistic latent schematic indexing (HdiPLSI) are reasonable approaches for balancing and clipping to the direction of health topics from various perspective data sources in health statistics clustering. Hadoop DiNNMF distributed model is achieved and covered by cosine metrics when exposed to visual clusters and good performance measures compared to other methods in a series of health conditions. This assistance briefly describes the public health structure (hashtags) in good condition in the country and tracks the evolution of the main health-related tweets for preliminary advice to the public.KeywordsHDFSSentiment analysisNatural language tool kit (NLTK)TF-IDFTwitter APIResource manager (RM)Name node (NN)Data node (DN)" @default.
• W3185793131 created "2021-08-02" @default.
• W3185793131 creator A5048412960 @default.
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• W3185793131 date "2021-07-16" @default.
• W3185793131 modified "2023-10-15" @default.
• W3185793131 title "Distributed Multimodal Aspective on Topic Model Using Sentiment Analysis for Recognition of Public Health Surveillance" @default.
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• W3185793131 doi "https://doi.org/10.1007/978-981-16-2126-0_38" @default.
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