Exploring Indicators of Developers' Sentiment Perceptions in Student Software Projects

2026-03-11

AI Summary

This study addresses the influence of psychological and contextual factors on developers’ perception of textual sentiment, an area where individual stability and underlying mechanisms remain poorly understood. Through four waves of longitudinal surveys, we systematically examine the variability in sentiment annotations assigned by student developers to decontextualized sentences during team projects, employing repeated-measures generalized estimating equations (GEE) and a meta-analysis of annotation uncertainty. Results reveal only moderate within-individual stability in sentiment perception. Trait affectivity and emotional reactivity significantly predict a more positive annotation bias, whereas project phase shows no systematic effect. The findings underscore that sentiment annotations are highly content-dependent and exhibit substantial intra-individual instability, highlighting the need for caution when interpreting annotation outcomes in software engineering sentiment analysis.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems

Abstract

Communication is a crucial social factor in the success of software projects, as positively or negatively perceived statements can influence how recipients feel and affect team collaboration through emotional contagion. Whether a developer perceives a written message as positive, negative, or neutral is likely shaped by multiple factors. In this paper, we investigate how mood traits and states, life circumstances, project phases, and group dynamics relate to the perception of text-based messages in software development. We conducted a four-round survey study with 81 students in team-based software projects. Across rounds, participants reported these factors and labeled 30 decontextualized statements for sentiment, including meta-data on labeling rationale and uncertainty. Our results show: (1) Sentiment perception is only moderately stable within individuals, and label changes concentrate on ambiguity-prone statements; (2) Correlation-level signals are small and do not survive global multiple-testing correction; (3) In statement-level repeated-measures models (GEE), higher mood trait and reactivity are associated with more positive (and less neutral) labeling, while predictors of negative labeling are weaker and at most trend-level (e.g., task conflict); (4) We find no clear evidence of systematic project-phase effects. Overall, sentiment perception varies within persons and is strongly statement-dependent. Although our study was conducted in an academic setting, the observed variability and ambiguity effects suggest caution when interpreting sentiment analysis outputs and motivate future work with contextualized, in-project communication.

Problem

• sentiment perception
• software development
• mood traits
• team communication
• emotional contagion

Innovation

• sentiment perception
• developer mood
• emotional contagion
• repeated-measures GEE
• annotation variability