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Text Analysis in Python for Social Scientists: Discovery and Exploration [Pehme köide]

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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.

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A practical guide to text analysis with Python: the intuition, the math, the code.
Introduction 1(2)
Background 3(1)
1 Prerequisites
3(2)
2 What's in a Word
5(14)
3 Regular Expressions
19(5)
4 Pointwise Mutual Information
24(3)
5 Representing Text
27(24)
Exploration: Finding Structure in the Data
50(1)
6 Matrix Factorization
51(8)
7 Clustering
59(6)
8 Language Models
65(7)
9 Topic Models
72(12)
Appendix A English Stopwords 84(1)
Appendix B Probabilities 85(5)
References 90