Automatic junk e-mail filtering based on latent content
J.R. Bellegarda, D. Naik, Kim E. A. Silverman · 2004
The explosion in unsolicited mass electronic mail (junk e-mail) over the past decade has sparked interest in automatic filtering solutions. Traditional techniques tend to rely on header analysis, keyword/keyphrase matching and analogous rule-based predicates, and/or some probabilistic model of text generation. This paper aims instead at deciding whether or not the latent subject matter is consistent with the user's interests. The underlying framework is latent semantic analysis: each e-mail is automatically classified against two semantic anchors, one for legitimate and one for junk messages. Experiments show that this approach is competitive with the state-of-the-art in e-mail classification, and potentially advantageous in real-world applications with high junk-to-legitimate ratios. The resulting technology has been successfully released in August 2002 as part of the e-mail client bundled with the MacOS 10.2 operating system.