Managing Emotion In The Workplace: An Empirical Study With Enterprise Instant Messaging

Abstract

ABSTRACTEnterprise Instant Messaging (EIM) has become an increasingly important tool for enterprises to operate efficiently and for the employees to communicate smoothly, especially with the recent outbreak of the pandemic. This means that employers and employees are having to adapt to new ways of working, e.g. teleworking or home-based working, and they could experience emotional stress, irritability and anxiety. However, few studies have used sentiment analysis to help employees manage their emotions and past studies mostly applied retrospective sentiment analysis on user-generated content as such as Twitter or the internal enterprise data. In this study we present an Employee Sentiment Analysis and Management System (ESAMS) that continuously monitors the emotions of the employees in real time by analyzing the conversations so the managerial members and the team members can actively manage their emotions or adjust their actions on the spot. As a proof-of-concept, we use Naïve Bayes as our sentiment classifier and achieve an average classification accuracy of 74%. The ESAMS was pilot-tested for one month by 10 participants, who were later interviewed as part of the evaluation. The results show that the ESAMS was helpful in improving team performance and team management.

Keywords

• Enterprise instant messaging
• emotion management
• machine learning
• sentiment analysis