Assessing e-mail intent and tasks in e-mail messages

In this paper, we analyze corporate e-mail messages as a medium to convey work tasks. Research indicates that categorization of e-mail could alleviate the common problem of information overload. Although e-mail clients provide possibilities of e-mail categorization, not many users spend effort on proper e-mail management. Since e-mail clients are often used for task management, we argue that intent- and task-based categorizations might be what is missing from current systems. We propose a taxonomy of tasks that are expressed through e-mail messages. With this taxonomy, we manually annotated two e-mail datasets (Enron and Avocado), and evaluated the validity of the dimensions in the taxonomy. Furthermore, we investigated the potential for automatic e-mail classification in a machine learning experiment. We found that approximately half of the corporate e-mail messages contain at least one task, mostly informational or procedural in nature. We show that automatic detection of the number of tasks in an e-mail message is possible with 71% accuracy. One important finding is that it is possible to use the e-mails from one company to train a classifier to classify e-mails from another company. Detecting how many tasks a message contains, whether a reply is expected, or what the spatial and time sensitivity of such a task is, can help in providing a more detailed priority estimation of the message for the recipient. Such a priority-based categorization can support knowledge workers in their battle against e-mail overload. (C) 2016 Elsevier Inc. All rights reserved.