  In this paper, the process of converting the Enron email dataset (the version
cited in the preprint) to thousands of features per email for a selected set of
2400 labelled emails is explained and evaluated. The final features are
tailored for Cosine distance so that the Cosine distance invertly reflect the
number of top indicative words of each email that are common between the two
emails in an explainable normalized fashion. The labelling is based on the leaf
folder name in the Enron email dataset (the version cited in the preprint)
folders tree and the 2400 emails selected consist 300 emails for each of the 8
labels. The evaluation is based on the accuracy of a k nearest neighbours
majority voting classification using Cosine distance. In addition to KNN
majority voting classification accuracy and confusion matrix, some statistics
for the process is reported. The KNN majority voting classification accuracy
using Cosine distance is 76.75% which shows at least some level of success
given the 8 labels involved. The result of conversion is 48557 features per
selected email out of which exactly 40 features per email are non-zero. The
result of conversion is a data set named MeeefTCD (Massive Enhanced Extracted
Email Features Tailored for Cosine Distance) available at
https://web.cs.dal.ca/~barahimi/data-sets/meeeftcd/ and on a github repository
mentioned in this paper.
