Managing Emotion In The Workplace: An Empirical Study With Enterprise Instant Messaging
Enterprise 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,...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2023.2297518 |
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| author | Shih-Wen Ke Chih-Fong Tsai Yi-Jun Chen |
| author_facet | Shih-Wen Ke Chih-Fong Tsai Yi-Jun Chen |
| author_sort | Shih-Wen Ke |
| collection | DOAJ |
| description | Enterprise 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. |
| format | Article |
| id | doaj-art-8652cc15f0fb49d9a66c44d4ba9f9e82 |
| institution | OA Journals |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-8652cc15f0fb49d9a66c44d4ba9f9e822025-08-20T01:56:56ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2023.2297518Managing Emotion In The Workplace: An Empirical Study With Enterprise Instant MessagingShih-Wen Ke0Chih-Fong Tsai1Yi-Jun Chen2Department of Information Management, National Central University, Taoyuan, TaiwanDepartment of Information Management, National Central University, Taoyuan, TaiwanDepartment of Information Management, National Central University, Taoyuan, TaiwanEnterprise 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.https://www.tandfonline.com/doi/10.1080/08839514.2023.2297518Enterprise instant messagingemotion managementmachine learningsentiment analysis |
| spellingShingle | Shih-Wen Ke Chih-Fong Tsai Yi-Jun Chen Managing Emotion In The Workplace: An Empirical Study With Enterprise Instant Messaging Applied Artificial Intelligence Enterprise instant messaging emotion management machine learning sentiment analysis |
| title | Managing Emotion In The Workplace: An Empirical Study With Enterprise Instant Messaging |
| title_full | Managing Emotion In The Workplace: An Empirical Study With Enterprise Instant Messaging |
| title_fullStr | Managing Emotion In The Workplace: An Empirical Study With Enterprise Instant Messaging |
| title_full_unstemmed | Managing Emotion In The Workplace: An Empirical Study With Enterprise Instant Messaging |
| title_short | Managing Emotion In The Workplace: An Empirical Study With Enterprise Instant Messaging |
| title_sort | managing emotion in the workplace an empirical study with enterprise instant messaging |
| topic | Enterprise instant messaging emotion management machine learning sentiment analysis |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2023.2297518 |
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