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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.
Text contains a wealth of information about 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 text: power, trust,
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.
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