Application of Latent Semantic Analysis in Accounting Research

Author:

Hutchison Paul D.1ORCID,George Benjamin2ORCID,Guragai Binod3ORCID

Affiliation:

1. University of North Texas

2. The University of Toledo

3. Texas State University

Abstract

ABSTRACT The purpose of this study is to review a text topic modeling methodology, latent semantic analysis (LSA), and provide researchers with the requisite knowledge to allow them to learn and implement their own accounting research study using LSA. The authors first provide a brief literature review of prior business and accounting research studies that have utilized the LSA methodology. Using a provided dataset, the authors present details of how to employ LSA in a research study by replicating the mechanics used in an LSA study conducted by Hutchison, Plummer, and George (2018b). Their intent is to present thorough guidance on data selection, the analysis platform, and the necessary steps needed to conduct LSA research. This article also briefly compares LSA with other topic modeling methodologies, presents several accounting research opportunities where LSA could be utilized, and outlines LSA’s limitations. Data Availability: Data are available from the public sources cited in the text; sample dataset is available for download, see footnote 5.

Publisher

American Accounting Association

Subject

Management of Technology and Innovation,Information Systems and Management,Human-Computer Interaction,Accounting,Information Systems,Software,Management Information Systems

Reference91 articles.

1. Using topic modeling methods for short-text data: A comparative analysis;Albalawi,;Frontiers in Artificial Intelligence,2020

2. A survey of topic modeling in text mining;Alghamdi,;International Journal of Advanced Computer Science and Applications,2015

3. Anaya, L. H. 2011. Comparing latent Dirichlet allocation and latent semantic analysis as classifiers. Doctoral dissertation, University of North Texas.

4. Angelov, D. 2020. Top2Vec: Distributed representations of topics. https://doi.org/10.48550/arXiv.2008.09470

5. Extending monitoring methods to textual data: A research agenda;Ashton,;Quality & Quantity,2014

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