Text and Voice Based Emotion Monitoring System

Abstract

An Emotion monitoring system for a call-center is proposed. It aims to simplify the tracking and management of emotions extracted from call center Employee-Customer conversations. The system is composed of four modules: Emotion Detection, Emotion Analysis and Report Generation, Database Manager, and User Interface. The Emotion Detection module uses Tone Analyzer to extract them for reliable emotion; it also performs the Utterance Analysis for detecting emotion. The 14 emotions detected by the tone analyzer are happy, joy, anger, sad and neutral, etc. The Emotion Analysis module performs classification into the 3 categories: Neutral, Anger and Joy. By using this category, it applies the point-scoring technique for calculating the Employee Score. This module also polishes the output of the Emotion Detection module to provide a more presentable output of a sequence of emotions of the Employee and the Customer. The Database Manager is responsible for the management of the database wherein it handles the creation, and update of data. The Interface module serves as the view and user interface for the whole system. The system is comprised of an Android application for conversation and a web application to view reports. The Android application was developed using Android Studio to maintain the modularity and flexibility of the system. The local server monitors the conversation, it displays the detected emotions of both the Customer and the Employee. On the other hand, the web application was constructed using the Django Framework to maintain its modularity and abstraction by using a model. It provides reports and analysis of the emotions expressed by the customer during conversations. Using the Model View Template (MVT) approach, the Emotion monitoring system is scalable, reusable and modular.