Abstract:
With the advent of business intelligence and big data, the machines have started sharing
the workload of humans in various domains of work. In this study the task of email
classification has been addressed through deep learning algorithms. The proposed system
contributes to Startford university’s project of automatic reply agent in order to
automatically respond to the queries of students without the intervention of human.
To achieve the said objective three deep learning algorithms (Multilayer Perceptron,
Recurrent Neural Network and Convolutional Neural Network) have been applied. The
proposed system has been trained and tested with 3 datasets of varying length, domain
and diversity respectively. Among the datasets, two are publicly available, i.e. ENRON
Intent and University Helpdesk however, the third dataset AskTeacher2018 is generated
by real time queries of students from the universities of Pakistan. The designed
algorithms have been optimized in two stages; basic network optimization (batch size,
epochs and no. of neurons etc.) and Advanced Network Optimization (Variation of the
models, Non linearity, batch normalization etc.). Over and above, other techniques of
testing and pre-processing have also been applied in order to improve the accuracy of
the results and impact of all these factors have been observed and analyzed. The finest
accuracy achieved for the three algorithms are 93.16% for ENRON Intent, 96.88% for
University Helpdesk and 98.86% for Ask Teacher2018.
Keywords: Deep Learning, Email Classification, Neural Network
