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Latent Semantic Analysis: five methodological recommendations

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  • Nicholas Evangelopoulos
  • Xiaoni Zhang
  • Victor R Prybutok

Abstract

The recent influx in generation, storage, and availability of textual data presents researchers with the challenge of developing suitable methods for their analysis. Latent Semantic Analysis (LSA), a member of a family of methodological approaches that offers an opportunity to address this gap by describing the semantic content in textual data as a set of vectors, was pioneered by researchers in psychology, information retrieval, and bibliometrics. LSA involves a matrix operation called singular value decomposition, an extension of principal component analysis. LSA generates latent semantic dimensions that are either interpreted, if the researcher's primary interest lies with the understanding of the thematic structure in the textual data, or used for purposes of clustering, categorization, and predictive modeling, if the interest lies with the conversion of raw text into numerical data, as a precursor to subsequent analysis. This paper reviews five methodological issues that need to be addressed by the researcher who will embark on LSA. We examine the dilemmas, present the choices, and discuss the considerations under which good methodological decisions are made. We illustrate these issues with the help of four small studies, involving the analysis of abstracts for papers published in the European Journal of Information Systems.

Suggested Citation

  • Nicholas Evangelopoulos & Xiaoni Zhang & Victor R Prybutok, 2012. "Latent Semantic Analysis: five methodological recommendations," European Journal of Information Systems, Taylor & Francis Journals, vol. 21(1), pages 70-86, January.
    • Handle: RePEc:taf:tjisxx:v:21:y:2012:i:1:p:70-86
    DOI: 10.1057/ejis.2010.61
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    Cited by:

    1. Jamal Al Qundus & Kosai Dabbour & Shivam Gupta & Régis Meissonier & Adrian Paschke, 2022. "Wireless sensor network for AI-based flood disaster detection," Annals of Operations Research, Springer, vol. 319(1), pages 697-719, December.
    2. Ahmed Gomaa & Yibai Li, 2022. "An Entrepreneurial Definition of the Blockchain Technology and a Stacked Layer Model of the ICO Marketplace Using the Text Mining Approach," JRFM, MDPI, vol. 15(12), pages 1-21, November.
    3. Manjul Gupta & Amulya Gupta & Karlene Cousins, 2022. "Toward the understanding of the constituents of organizational culture: The embedded topic modeling analysis of publicly available employee‐generated reviews of two major U.S.‐based retailers," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3668-3686, October.
    4. Lucian Visinescu & Nicholas Evangelopoulos, 2024. "Designing adaptive feedback mechanisms with text mining capabilities: An illustration on eBay," Electronic Markets, Springer;IIM University of St. Gallen, vol. 34(1), pages 1-21, December.
    5. Katalin Czakó & Csilla Polster & Santi Setyaningsih & Tihana Vasic, 2023. "The role of the environment in entrepreneurial propensity of youngsters’ business ideas," Journal of Innovation and Entrepreneurship, Springer, vol. 12(1), pages 1-19, December.
    6. Jungsu Kim & Sukjun Lee, 2023. "Collective Adaptive Responses Through Coping and Sensemaking Under Stress," SAGE Open, , vol. 13(4), pages 21582440231, October.

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