Text Analysis in Python for Social Scientists

Prediction and Classification Elements in Quantitative and Computational Methods for the Social Sciences

Abstract:

Text contains a wealth of information about a wide variety of sociocultural constructs. Automated prediction methods can infer these quantities (sentiment analysis is probably the most well-known application). However, there is virtually no limit to the kinds of things we can predict from the text: power, trust, and misogyny are all signaled in language. These algorithms easily scale to corpus sizes infeasible for manual analysis. Prediction algorithms have become steadily more powerful, especially with the advent of neural network methods. However, applying these techniques usually requires profound programming knowledge and machine learning expertise. As a result, many social scientists do not apply them. This Element provides the working social scientist with an overview of the most common methods for text classification, an intuition of their applicability, and Python code to execute them. It covers both the ethical foundations of such work as well as the emerging potential of neural network methods.

(Dirk Hovy)

https://www.cambridge.org/core/elements/abs/text-analysis-in-python-for-social-scientists/BFAB0A3604C7E29F6198EA2F7941DFF3

https://www.amazon.com/Text-Analysis-Python-Social-Scientists/dp/1108958508

https://github.com/dirkhovy/text_analysis_for_social_science