Judging the emotional states of customer service staff in the workplace: A multimodal dataset analysis.

Background: Emotions play a decisive and central role in the workplace, especially in the service-oriented enterprises. Due to the highly participatory and interactive nature of the service process, employees' emotions are usually highly volatile during the service delivery process, which can have a negative impact on business performance. Therefore, it is important to effectively judge the emotional states of customer service staff. Methods: We collected data on real-life work situations of call center employees in a large company. Three consecutive studies were conducted: first, the emotional states of 29 customer service staff were videotaped by wide-angle cameras. In Study 1, we constructed scoring criteria and auxiliary tools of picture-type scales through a free association test. In Study 2, two groups of experts were invited to evaluate the emotional states of customer service staff. In Study 3, based on the results in Study 2 and a multimodal emotional recognition method, a multimodal dataset was constructed to explore how each modality conveys the emotions of customer service staff in workplace. Results: Through the scoring by 2 groups of experts and 1 group of volunteers, we first developed a set of scoring criteria and picture-type scales with the combination of SAM scale for judging the emotional state of customer service staff. Then we constructed 99 (out of 297) sets of stable multimodal emotion datasets. Based on the comparison among the datasets, we found that voice conveys emotional valence in the workplace more significantly, and that facial expressions have more prominant connection with emotional arousal. Conclusion: Theoretically, this study enriches the way in which emotion data is collected and can provide a basis for the subsequent development of multimodal emotional datasets. Practically, it can provide guidance for the effective judgment of employee emotions in the workplace.