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Latent Semantic Analysis (LSA) in Python

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

Abstract

This chapter presents the application of latent semantic analysis (LSA) in Python as a complement to Chap.  6, which covers semantic space modeling and LSA. In this chapter, we will present how to implement text analysis with LSA through annotated code in Python. In this example, we will run LSA over a dataset that includes 401 instances of both online and offline review sources from the Areias do Seixo Eco-Resort (Data available at https://archive.ics.uci.edu/ml/datasets/Eco-hotel).

Keywords

Python Latent semantic analysis Text analytics Text mining 

Notes

Acknowledgment

The authors thank Jorge Fresneda Fernandez, Assistant Professor of Marketing at the Martin Tuchman School of Management, New Jersey Institute of Technology for contributing this chapter to the book.

Further Reading

  1. To learn more about the open-source Python software, visit https://wiki.python.org/moin/BeginnersGuide/Download

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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