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Chapter 9
We can see that authors are predicted correctly in most cases—there is a clear
diagonal line with high values. There are some large sources of error though (darker
values are larger): e-mails from user baughman-d are typically predicted as being
from reitmeyer-j for instance.
Summary
In this chapter, we looked at the text mining-based problem of authorship
attribution. To perform this, we analyzed two types of features: function words
and character n-grams. For function words, we were able to use the bag-of-words
model—simply restricted to a set of words we chose beforehand. This gave us the
frequencies of only those words. For character n-grams, we used a very similar
workflow using the same class. However, we changed the analyzer to look at
characters and not words. In addition, we used n-grams that are sequences of n
tokens in a row—in our case characters. Word n-grams are also worth testing in
some applications, as they can provide a cheap way to get the context of how a
word is used.
For classification, we used SVMs that optimize a line of separation between the
classes based on the idea of finding the maximum margin. Anything above the line is
one class and anything below the line is another class. As with the other classification
tasks we have considered, we have a set of samples (in this case, our documents).
We then used a very messy dataset, the Enron e-mails. This dataset contains lots of
artefacts and other issues. This resulted in a lower accuracy than the books dataset,
which was much cleaner. However, we were able to choose the correct author more
than half the time, out of 10 possible authors.
In the next chapter, we consider what we can do if we don't have target classes.
This is called unsupervised learning, an exploratory problem rather than a prediction
problem. We also continue to deal with messy text-based datasets.
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