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Email Classification

Email Classification. Results for Folder Classification on Enron Dataset. Overall Goals. To help users manage large volumes of email. … by helping them to sort their email into folders. Immediate Goals. To establish an credible test corpus

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Email Classification

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  1. Email Classification Results for Folder Classification on Enron Dataset

  2. Overall Goals • To help users manage large volumes of email. • …by helping them to sort their email into folders.

  3. Immediate Goals • To establish an credible test corpus • To create baseline results for email classification • To analyze possible future techniques

  4. The “Enron” Corpus • Previous email classification experiments have used “toy” collections. • Enron emails are collected from actual business users. • Made public through legal proceedings.

  5. The Enron Corpus • 158 users • 200,399 emails • Average of 757 emails per user

  6. Enron Data Analysis • Most users do use folders to classify their email. • Some users with many emails still have few folders. • Users with more emails tend to have more email in each folder.

  7. Representation • From • To, CC • Subject • Body • Date/Time? • Thread? • Attachments? • etc…?

  8. Approaches • Using a bag-of-words classification decision email data “bag of words” SVM

  9. Approaches • Using separate SVMs for each section LLSF classification decision email data SVMs

  10. Approach • Data was split in half, chronologically. • A “flat” approach was used. (not hierarchical) • An SVM was trained for each folder for each user for each field. • The SVM for each folder was trained using all of the emails for that user. • Combination weights were found with a regression for each folder. • Thresholding was performed for optimal F1 score, using the “scut” method.

  11. “Enron” Results Analysis • Obviously some data fields are more useful than others. • Unsurprisingly, the “To, CC” data is the least useful. • Body is the most useful field, followed closely by sender. • Using all fields works better than using any particular field alone. • Linearly combining fields works better than bag-of-words approach. • Because it’s SVM, the linear weights are not directly interpretable.

  12. Enron Results Analysis • F1 classification score is unrelated to the number of emails a user has.

  13. Enron Results Analysis • F1 score is somewhat correlated with the number of folders a user has. • Emails are much harder to classify for users with many folders.

  14. Enron Thread Analysis • 200,399 messages • 101,786 threads • 30,091 non-trivial threads • 61.63% messages are in non-trivial threads • Average of 4.1 messages/thread • Median of 2 messages/thread

  15. Enron Thread Analysis • Largest threads are most potentially useful. But, the largest threads are the least common. • Threads are also redundant with other kinds of evidence. Since threads are detected by subject and sender, much of the thread information is redundant. Also, emails in the same thread tend to have similar bodies. • Largest thread in the Enron corpus is 1124 copies of the same message…all in the “Deleted Items” folder for a particular user!

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