Print Email Facebook Twitter Combining textual and non-textual features for e-mail importance estimation Title Combining textual and non-textual features for e-mail importance estimation Author Sappelli, M. Verberne, S. Kraaij, W. Publication year 2013 Abstract In this work, we present a binary classification problem in which we aim to identify those email messages that the receiver will reply to. The future goal is to develop a tool that informs a knowledge worker which emails are likely to need a reply. The Enron corpus was used to extract training examples. We analysed the word n-grams that characterize the messages that the receiver replies to. Additionally, we compare a Naive Bayes classifier to a decision tree classifier in the task of distinguishing replied from non-replied e-mails. We found that textual features are well-suited for obtaining high accuracy. However, there are interesting differences between recall and precision for the various feature selections. Subject Communication & InformationMNS - Media & Network ServicesTS - Technical Sciences To reference this document use: http://resolver.tudelft.nl/uuid:afe868e6-a12c-454f-be7f-6d0fe05e9b72 TNO identifier 745688 Source Proceedings of the 25th Benelux Conference on Artificial Intelligence, 2013, 1-7 Document type conference paper Files To receive the publication files, please send an e-mail request to TNO Library.