Email Surveillance Using Non-negative Matrix Factorization
Michael W. Berry () and
Murray Browne ()
Additional contact information
Michael W. Berry: University of Tennessee
Murray Browne: University of Tennessee
Computational and Mathematical Organization Theory, 2005, vol. 11, issue 3, No 5, 249-264
Abstract:
Abstract In this study, we apply a non-negative matrix factorization approach for the extraction and detection of concepts or topics from electronic mail messages. For the publicly released Enron electronic mail collection, we encode sparse term-by-message matrices and use a low rank non-negative matrix factorization algorithm to preserve natural data non-negativity and avoid subtractive basis vector and encoding interactions present in techniques such as principal component analysis. Results in topic detection and message clustering are discussed in the context of published Enron business practices and activities, and benchmarks addressing the computational complexity of our approach are provided. The resulting basis vectors and matrix projections of this approach can be used to identify and monitor underlying semantic features (topics) and message clusters in a general or high-level way without the need to read individual electronic mail messages.
Keywords: electronic mail; Enron collection; non-negative matrix factorization; surveillance; topic detection; constrained least squares (search for similar items in EconPapers)
Date: 2005
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://link.springer.com/10.1007/s10588-005-5380-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:comaot:v:11:y:2005:i:3:d:10.1007_s10588-005-5380-5
Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10588
DOI: 10.1007/s10588-005-5380-5
Access Statistics for this article
Computational and Mathematical Organization Theory is currently edited by Terrill Frantz and Kathleen Carley
More articles in Computational and Mathematical Organization Theory from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().