Monitoring email transaction logs by text-mining email contents

Monitoring every single email takes a lot of effort especially when the size of email transaction log is very large. This study proposed to find a wise option to monitor only the contents of important emails. Depth First Search algorithm, multi-digraph, email scoring model, WordNet, and Vector Space Model are used to create a model for filtering important emails and mining email contents. The findings showed that using email filtering module together with term enhancing module can help in reducing the processing time and keeping high precision and recall values of the system.