Structure in the Enron Email Dataset

We investigate the structures present in the Enron email dataset using singular value decomposition and semidiscrete decomposition. Using word frequency profiles we show that messages fall into two distinct groups, whose extrema are characterized by short messages and rare words versus long messages and common words. It is surprising that length of message and word use pattern should be related in this way. We also investigate relationships among individuals based on their patterns of word use in email. We show that word use is correlated to function within the organization, as expected. We also show that word use among those involved in alleged criminal activity may be slightly distinctive.