Book chapter
Latent Semantic Analysis (LSA) in Python
Practical Text Analytics, pp 221-242
20 Oct 2018
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).
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Details
- Title
- Latent Semantic Analysis (LSA) in Python
- Creators
- Murugan Anandarajan - Drexel UniversityChelsey Hill - Montclair State UniversityThomas Nolan - Mercury Systems (United States)
- Publication Details
- Practical Text Analytics, pp 221-242
- 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
- 991019551544804721