Book chapter
Semantic Space Representation and Latent Semantic Analysis
Practical Text Analytics, pp 77-91
20 Oct 2018
Abstract
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.
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Details
- Title
- Semantic Space Representation and Latent Semantic Analysis
- Creators
- Murugan Anandarajan - Drexel UniversityChelsey Hill - Montclair State UniversityThomas Nolan - Mercury Systems (United States)
- Publication Details
- Practical Text Analytics, pp 77-91
- Series
- Advances in Analytics and Data Science
- Publisher
- Springer International Publishing; Cham
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems); Bennett S. LeBow College of Business; Television (and Media) Management; Drexel University
- Other Identifier
- 991019551686904721