Deep Learning based Intelligent E-mail Autoresponder. Issue 1 (January 2021)

• Record Type: Journal Article
• Authors: Lakshmi Priya, B.; Jayalakshmy, S.; Saraswathi, D.; Kumar, V.; Dinesh, E.
• Is Part Of: Journal of physics. Volume 1717:Issue 1(2021)
• Publication Date: 2021-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1717/1/012010
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Handling huge volume of emails is a very challenging task in the customer support applications and an automated email responding system will be of great help. In this paper, an intelligent email autoresponder system is developed which either attempts to respond to the incoming emails from various category of customers or generate token for service request to address the issue manually by an expert member. First, based on the content the system has to predict whether the mail belong to the category of auto responding or to invoke a service request. This classification of email is carried out using long short term (LSTM) and bi-directional LSTM networks (Bi-LSTM) networks and the classification performance is analyzed. The results presented in this work show that the Bi-LSTM classifier outperforms LSTM network.