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Topic Modeling
In the previous chapter we clustered texts into groups. This is a very useful tool, but
it is not always appropriate. Clustering results in each text belonging to exactly one
cluster. This book is about machine learning and Python. Should it be grouped with
other Python-related works or with machine-related works? In the paper book age,
a bookstore would need to make this decision when deciding where to stock it. In
the Internet store age, however, the answer is that this book is both about machine
learning and Python, and the book can be listed in both sections. We will, however,
not list it in the food section.
In this chapter, we will learn methods that do not cluster objects, but put them into
a small number of groups called topics. We will also learn how to derive between
topics that are central to the text and others only that are vaguely mentioned (this
book mentions plotting every so often, but it is not a central topic such as machine
learning is). The subfield of machine learning that deals with these problems is
called topic modeling.
Latent Dirichlet allocation (LDA)
LDA and LDA: unfortunately, there are two methods in machine learning with
the initials LDA: latent Dirichlet allocation, which is a topic modeling method; and
linear discriminant analysis, which is a classification method. They are completely
unrelated, except for the fact that the initials LDA can refer to either. However, this
can be confusing. Scikit-learn has a submodule, sklearn.lda, which implements
linear discriminant analysis. At the moment, scikit-learn does not implement latent
Dirichlet allocation.
The simplest topic model (on which all others are based) is latent Dirichlet
allocation (LDA). The mathematical ideas behind LDA are fairly complex,
and we will not go into the details here.