Title: Intelligent model to improve the efficacy of healthcare content marketing by auto-tagging and exploring the veracity of content using opinion mining

Authors: S. Sri Hari; S. Porkodi; R. Saranya; N. Vijayakumar

Addresses: Department of Computer Science, Illinois Institute of Technology, Chicago, USA ' Department of Business Studies, University of Technology and Applied Sciences (HCT), Muscat, Sultanate of Oman ' Department of Business Studies, University of Technology and Applied Sciences (HCT), Muscat, Sultanate of Oman ' Department of Computer Science, Technical Administrative Training Institute, Muscat, Sultanate of Oman

Abstract: Digital content is favourably useful for sales and marketing industries and the healthcare industry is no exception. Most user seeks medical advice for treatment as well as health information by crawling and analysing the information on the web. The digital content must be highly trustworthy in the healthcare domain as it involves the health of a human. This paper presents an intelligent model that intends to analyse the relevancy and reliability of the content by applying sentimental analysis to the readers' comments. The model applies relevancy computation for analysing the content, enhanced lexicon analyser for scoring the words, maximum entropy model for classification and computes the veracity score. Based on the computed score, the content can be recommended to the readers and auto-tagged effectively. The result analysis made with the healthcare contents proves the effective performance of the proposed model in analysing the reliability of the information.

Keywords: healthcare content marketing; opinion mining; sentiment analysis; content relevancy; content reliability; auto-tagging; maximum entropy model.

DOI: 10.1504/IJEMR.2024.136978

International Journal of Electronic Marketing and Retailing, 2024 Vol.15 No.2, pp.240 - 260

Received: 03 Mar 2022
Accepted: 17 May 2022
Published online: 01 Mar 2024 *

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