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Email Surveillance Using Non-negative Matrix Factorization

Author

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  • Michael W. Berry

    (University of Tennessee)

  • Murray Browne

    (University of Tennessee)

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.

Suggested Citation

  • Michael W. Berry & Murray Browne, 2005. "Email Surveillance Using Non-negative Matrix Factorization," Computational and Mathematical Organization Theory, Springer, vol. 11(3), pages 249-264, October.
    • Handle: RePEc:spr:comaot:v:11:y:2005:i:3:d:10.1007_s10588-005-5380-5
    DOI: 10.1007/s10588-005-5380-5
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    References listed on IDEAS

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    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    2. P. S. Keila & D. B. Skillicorn, 2005. "Structure in the Enron Email Dataset," Computational and Mathematical Organization Theory, Springer, vol. 11(3), pages 183-199, October.
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    Cited by:

    1. Norikazu Takahashi & Jiro Katayama & Masato Seki & Jun’ichi Takeuchi, 2018. "A unified global convergence analysis of multiplicative update rules for nonnegative matrix factorization," Computational Optimization and Applications, Springer, vol. 71(1), pages 221-250, September.
    2. Norikazu Takahashi & Ryota Hibi, 2014. "Global convergence of modified multiplicative updates for nonnegative matrix factorization," Computational Optimization and Applications, Springer, vol. 57(2), pages 417-440, March.
    3. Jianhong Luo & Minjuan Chai & Xuwei Pan, 2021. "Identification of Research Priorities during the COVID-19 Pandemic: Implications for Its Management," IJERPH, MDPI, vol. 18(24), pages 1-15, December.

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