Recursive data mining for role identification
Vineet Chaoji, Apirak Hoonlor, Boleslaw Karol Szymanski · 2008
We present a text mining approach that enables an extension of a standard authorship assessment problem (the problem in which an author of a text needs to be established) to role identification in communications within some Internet community. More precisely, we want to recognize a group of authors communicating in a specific role within such a community rather than a single author. The challenge here is that the same author may participate in different roles in communications within the group, in each role having different authors as peers. An additional challenge of our problem is the length of communications. Each individual exchange in our intended domain, communications within an Internet community, is relatively short, in the order of several dozens of words, so standard text mining approaches may fail. An example of such a problem is recognizing roles in a collection of emails from an organization in which middle level managers communicate both with superiors and subordinates. To validate our approach we use the Enron email dataset which is such a collection.