Investigating Unstructured Texts with Latent Semantic Analysis

  • Fridolin Wild
  • Christina Stahl
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Latent semantic analysis (LSA) is an algorithm applied to approximate the meaning of texts, thereby exposing semantic structure to computation. LSA combines the classical vector-space model — well known in computational linguistics — with a singular value decomposition (SVD), a two-mode factor analysis. Thus, bag-of-words representations of texts can be mapped into a modified vector space that is assumed to reflect semantic structure. In this contribution the authors describe the lsa package for the statistical language and environment R and illustrate its proper use through examples from the areas of automated essay scoring and knowledge representation.


Singular Value Decomposition Weighting Scheme Document Collection Latent Semantic Analysis Semantic Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Fridolin Wild
    • 1
  • Christina Stahl
    • 1
  1. 1.Institute for Information Systems and New MediaVienna University of Economics and Business AdministrationViennaAustria

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