Latent Semantic Analysis: A Big Data Opportunity for Tax Research

Paul D. Hutchison · C. Plummer · Benjamin George

2 citations0/0 files storedThe Contemporary Tax Journal

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Computational and Text Analysis Methods scientometrics and bibliometrics research Topic Modeling

Abstract

The underlying purpose of this article is to discuss latent semantic analysis (LSA) and its application and research opportunities in tax research.LSA is a Big Data research methodology that can assist researchers in identifying the underlying themes/topics in data.To illustrate, the authors apply LSA to almost 2,300 abstracts of articles in Advances in Taxation, The Journal of the American Taxation Association, and National Tax Journal to identify the major topics in academic tax research over the period 1979-2015 and examine differences across journals and across time.The authors also discuss how researchers can apply LSA methodology to specific tax and accounting topics, providing research opportunities that heretofore may have previously been impractical.

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2005Three Approaches to Qualitative Content Analysis 1984Content Analysis: An Introduction to its Methodology.2019Content Analysis: An Introduction to Its Methodology 1990Basic Content Analysis 2002Content Analysis: Review of Methods and Their Applications in Nutrition Education 1979On the Measurement of Corporate Social Responsibility: Self-Reported Disclosures as a Method of Measuring Corporate Social Involvement 2008Are Investors Influenced By How Earnings Press Releases Are Written?2008Uncovering the intellectual core of the information systems discipline

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