Discovering accurate deep learning based predictive models for automatic customer support ticket classification

Ticket Management Systems are widespread in disparate kinds of companies and organizations, as they represent a fundamental tool for handling customer requests and issues in an efficient and effective manner. In particular, accurately categorizing incoming tickets is a key task in real-life application settings (e.g., helpdesk/CRM systems and bug tracking systems), in order to improve ticket processing efficiency and effectiveness (e.g., in terms of customer satisfaction). In this work, we propose a comprehensive ticket-categorization analysis that relies on inducing and exploiting a heterogeneous ensemble of deep learning architectures, in addition to a range of functionalities for acquiring, integrating and pre-processing ticket-related information coming from different channels (e.g. mail, chat, web form, etc.). Experimental results conducted on the specific application scenario concerning the data of a publicly available ticket-mining dataset have proven the effectiveness of the framework in different ticket categorization tasks.