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Multi-viewpoints visual models for efficient modeling and analysis of Twitter based health-care services

Noorullah Renigunta Mohammed (Department of CSE, Koneru Lakshmaiah Education Foundation, Guntur, India and Department of CSE, Institute of Aeronautical Engineering, Hyderabad, India)
Moulana Mohammed (Department of CSE, Koneru Lakshmaiah Education Foundation, Guntur, India)

International Journal of Pervasive Computing and Communications

ISSN: 1742-7371

Article publication date: 21 October 2021

Issue publication date: 27 January 2022

47

Abstract

Purpose

The purpose of this study for eHealth text mining domains, cosine-based visual methods (VM) assess the clusters more accurately than Euclidean; which are recommended for tweet data models for clusters assessment. Such VM determines the clusters concerning a single viewpoint or none, which are less informative. Multi-viewpoints (MVP) were used for addressing the more informative clusters assessment of health-care tweet documents and to demonstrate visual analysis of cluster tendency.

Design/methodology/approach

In this paper, the authors proposed MVP-based VM by using traditional topic models with visual techniques to find cluster tendency, partitioning for cluster validity to propose health-care recommendations based on tweets. The authors demonstrated the effectiveness of proposed methods on different real-time Twitter health-care data sets in the experimental study. The authors also did a comparative analysis of proposed models with existing visual assessment tendency (VAT) and cVAT models by using cluster validity indices and computational complexities; the examples suggest that MVP VM were more informative.

Findings

In this paper, the authors proposed MVP-based VM by using traditional topic models with visual techniques to find cluster tendency, partitioning for cluster validity to propose health-care recommendations based on tweets.

Originality/value

In this paper, the authors proposed multi-viewpoints distance metric in topic model cluster tendency for the first time and visual representation using VAT images using hybrid topic models to find cluster tendency, partitioning for cluster validity to propose health-care recommendations based on tweets.

Keywords

Acknowledgements

Funding: This research did not receive any financial grant/funding from any agency in public, commercial or not-for-profit sectors to conduct this study. The author also sincerely appreciates the editor and reviewers for their time and valuable comments.

Conflict of interest: The author confirms that there is no conflict of interest to declare for this publication.

Citation

Renigunta Mohammed, N. and Mohammed, M. (2022), "Multi-viewpoints visual models for efficient modeling and analysis of Twitter based health-care services", International Journal of Pervasive Computing and Communications, Vol. 18 No. 1, pp. 114-142. https://doi.org/10.1108/IJPCC-06-2021-0140

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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