Text analysis in Python for social scientists : discovery and exploration
著者
書誌事項
Text analysis in Python for social scientists : discovery and exploration
（Cambridge elements, . Elements in quantitative and computational methods for the social sciences / edited by R. Michael Alvarez,
Cambridge University Press, 2020
- : pbk
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注記
Includes bibliographical references (p. [90]-95)
内容説明・目次
内容説明
Text is everywhere, and it is a fantastic resource for social scientists. However, because it is so abundant, and because language is so variable, it is often difficult to extract the information we want. There is a whole subfield of AI concerned with text analysis (natural language processing). Many of the basic analysis methods developed are now readily available as Python implementations. This Element will teach you when to use which method, the mathematical background of how it works, and the Python code to implement it.
目次
- 1. Prerequisites
- 2. What's in a Word
- 3. Regular Expressions
- 4. Pointwise Mutual Information
- 5. Representing Text
- 6. Matrix Factorization
- 7. Clustering
- 8. Language Models
- 9. Topic Models.
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