Semantic Space Representation and Latent Semantic Analysis

  • Murugan Anandarajan
  • Chelsey Hill
  • Thomas Nolan
Part of the Advances in Analytics and Data Science book series (AADS, volume 2)


In this chapter, we introduce latent semantic analysis (LSA), which uses singular value decomposition (SVD) to reduce the dimensionality of the document-term representation. This method reduces the large matrix to an approximation that is made up of fewer latent dimensions that can be interpreted by the analyst. Two important concepts in LSA, cosine similarity and queries, are explained. Finally, we discuss decision-making in LSA.


Latent semantic analysis (LSA) Singular value decomposition (SVD) Latent semantic indexing (LSI) Cosine similarity Queries 


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Further Reading

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Murugan Anandarajan
    • 1
  • Chelsey Hill
    • 2
  • Thomas Nolan
    • 3
  1. 1.LeBow College of BusinessDrexel UniversityPhiladelphiaUSA
  2. 2.Feliciano School of BusinessMontclair State UniversityMontclairUSA
  3. 3.Mercury Data ScienceHoustonUSA

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