Building Machine Learning Systems with Python - Richert, Coelho

Topic Modeling
Although daunting at first glance, we can clearly see that the topics are not just
random words, but are connected. We can also see that these topics refer to older
news items, from when the Soviet union still existed and Gorbachev was its Secretary
General. We can also represent the topics as word clouds, making more likely words
larger For example, this is the visualization of a topic, which deals with the Middle
East and politics:
We can also see that some of the words should perhaps be removed (for example,
the word I) as they are not so informative (stop words). In topic modeling, it is
important to filter out stop words, as otherwise you might end up with a topic
consisting entirely of stop words, which is not very informative. We may also wish
to preprocess the text to stems in order to normalize plurals and verb forms. This
process was covered in the previous chapter, and you can refer to it for details. If you
are interested, you can download the code from the companion website of the book
and try all these variations to draw different pictures.
Building a word cloud like the one in the previous screenshot can
be done with several different pieces of software. For the previous
graphic, I used the online tool wordle (http://www.wordle.net),
which generates particularly attractive images. Since I only had a
few examples, I copy and pasted the list of words manually, but it is
possible to use it as a web service and call it directly from Python.
Comparing similarity in topic space
Topics can be useful on their own to build small vignettes with words that are in the
previous screenshot. These visualizations could be used to navigate a large collection
of documents and, in fact, they have been used in just this way.
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