Using Naïve Bayes Method to Classify Text-Based Email
Lanlan Kang, Ruey‐Shun Chen, Yeh-Cheng Chen, Wenliang Cao · 2018
The World Talk Corporation estimates that over 60 million business people use e-mail. Many more use e-mail purely on a personal basis and the pool of e-mail users is growing daily. And yet, automated techniques for learning to filter e-mail have yet to significantly affect the e-mail market. Here, I attack problems that plague practical e-mail filtering and suggest solutions that will bring us closer to the acceptance of using automated classification techniques to filter personal e-mail. I also present a filtering system, BETSY, that is both effective and efficient, and which has been adapted to a popular e-mail client. Results are presented from a number of experiments and show that a system such as BETSY could become a useful and valuable part of any e-mail client.