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Exploring Big Data Analytic Approaches to Cancer Blog Text Analysis

Exploring Big Data Analytic Approaches to Cancer Blog Text Analysis
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Author(s): Viju Raghupathi (Koppelman School of Business, Brooklyn College of the City University of New York, Brooklyn, USA), Yilu Zhou (Gabelli School of Business, Fordham University, New York, USA) and Wullianallur Raghupathi (Gabelli School of Business, Fordham University, New York, USA)
Copyright: 2022
Pages: 21
Source title: Research Anthology on Big Data Analytics, Architectures, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-3662-2.ch090

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Abstract

In this article, the authors explore the potential of a big data analytics approach to unstructured text analytics of cancer blogs. The application is developed using Cloudera platform's Hadoop MapReduce framework. It uses several text analytics algorithms, including word count, word association, clustering, and classification, to identify and analyze the patterns and keywords in cancer blog postings. This article establishes an exploratory approach to involving big data analytics methods in developing text analytics applications for the analysis of cancer blogs. Additional insights are extracted through various means, including the development of categories or keywords contained in the blogs, the development of a taxonomy, and the examination of relationships among the categories. The application has the potential for generalizability and implementation with health content in other blogs and social media. It can provide insight and decision support for cancer management and facilitate efficient and relevant searches for information related to cancer.

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