Deep Learning Based Complaint Classification for Indonesia Telecommunication Company’s Call Center Shinta Devi Lukitasari, Fadhil Hidayat
School of Electrical Engineering and Informatics
Bandung Institute of Technology
Abstract
The preliminary research was held to utilize the call center conversations records from a broadband telecommunications company in Indonesia. There is a need from company to classify customer’s complaints automatically by a system to minimize human errors and at once streamline the business processes and resources. Natural Language Processing (NLP), as an integral part of artificial intelligence (AI), empower machines to understand human languages for performing beneficial tasks. The growth of deep learning is the main driver behind NLP for performing various practical applications and business, therefore deep learning is expected to overcome the problems encountered. This paper explains the methods used in designing the classification systems based on deep learning. Literature review is conducted to find the proper algorithm used in classifying problems. Furthermore, it also explains the stages performed in preparing the data and building the system model. From experiments conducted, it can be stated that the RNN algorithm can be used in the classification of customer complaints with the results shown by the accuracy value of the model.
Keywords: call center, deep learning, intent, complaint, classification, RNN
