Emotions Extracted from Text vs. True Emotionsxe2x80x93An Empirical Evaluation in SE Context Emotion awareness research in SE context has been growing in recent years. Currently, researchers often rely on textual communication records to extract emotion states using natural language processing techniques. However, how well these extracted emotion states reflect peopleu0027s real emotions has not been thoroughly investigated. In this paper, we report a multilevel, longitudinal empirical study with 82 individual members in 27 project teams. We collected their selfreported retrospective emotion states on a weekly basis during their yearlong projects and also extracted corresponding emotions from the textual communication records. We then model and compare the dynamics of these two types of emotions using multiple statistical and time series analysis methods. Our analyses yield a rich set of findings. The most important one is that the dynamics of emotions extracted using textbased algorithms often do not well reflect the dynamics of selfreported retrospective emotions. Besides, the extracted emotions match selfreported retrospective emotions better at the teamlevel. Our results also suggest that individual personalities and the teamu0027s emotion display norms significantly impact the matchmismatch. Our results should warn the research community about the limitations and challenges of applying textbased emotion recognition tools in SE research.

An Approach for Investigating Emotion Dynamics in Software Development Emotion awareness is critical to interpersonal communication, including that in software development. The SE community has studied emotion in software development using isolated emotion states but it has not considered the dynamic nature of emotion. To investigate the emotion dynamics, SE community needs an effective approach. In this paper, we propose such an approach which can automatically collect project teamsu0027 communication records, identify the emotions and their intensities in them, model the emotion dynamics into time series, and provide efficient data management. We demonstrate that this approach can provide endtoend support for various emotion awareness research and practices through automated data collection, modeling, storage, analysis, and presentation using the IPythonu0027s project data on GitHub.