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Authorship Attribution
We can reuse the grid search from our previous code. All we need to do is specify the
new feature extractor in a new pipeline:
pipeline = Pipeline([('feature_extraction', CountVectorizer(analyzer='
char', ngram_range=(3, 3))),
('classifier', grid)
])
scores = cross_val_score(pipeline, documents, classes, scoring='f1')
print("Score: {:.3f}".format(np.mean(scores)))
There is a lot of implicit overlap between function words and character
n-grams, as character sequences in function words are more likely to
appear. However, the actual features are very different and character
n-grams capture punctuation, which function words do not. For example,
a character n-gram includes the full stop at the end of a sentence, while a
function word-based method would only use the preceding word itself.
Using the Enron dataset
Enron was one of the largest energy companies in the world in the late 1990s,
reporting revenue over $100 billion. It has over 20,000 staff and—as of the year
2000—there seemed to be no indications that something was very wrong.
In 2001, the Enron Scandal occurred, where it was discovered that Enron was
undertaking systematic, fraudulent accounting practices. This fraud was deliberate,
wide-ranging across the company, and for significant amounts of money. After this
was publicly discovered, its share price dropped from more than $90 in 2000 to less
than $1 in 2001. Enron shortly filed for bankruptcy in a mess that would take more
than 5 years to finally be resolved.
As part of the investigation into Enron, the Federal Energy Regulatory Commission
in the United States made more than 600,000 e-mails publicly available. Since then,
this dataset has been used for everything from social network analysis to fraud
analysis. It is also a great dataset for authorship analysis, as we are able to extract
e-mails from the sent folder of individual users. This allows us to create a dataset
much larger than many previous datasets.
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