Using social media and personality traits to assess software developers' emotional polarity

Leo Silva; Marília Gurgel de Castro; Miriam Bernardino Silva; Milena Santos; Uirá Kulesza; Margarida Lima; Henrique Madeira

doi:10.7717/peerj-cs.1498

Using social media and personality traits to assess software developers' emotional polarity

PeerJ Comput Sci. 2023 Sep 27:9:e1498. doi: 10.7717/peerj-cs.1498. eCollection 2023.

Authors

Leo Silva¹, Marília Gurgel de Castro², Miriam Bernardino Silva², Milena Santos², Uirá Kulesza³, Margarida Lima², Henrique Madeira¹

Affiliations

¹ Centre of Informatics and Systems, University of Coimbra, Coimbra, Portugal.
² Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal.
³ Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil.

Abstract

Although human factors (e.g., cognitive functions, behaviors and skills, human error models, etc.) are key elements to improve software development productivity and quality, the role of software developers' emotions and their personality traits in software engineering still needs to be studied. A major difficulty is in assessing developers' emotions, leading to the classic problem of having difficulties understanding what cannot be easily measured. Existing approaches to infer emotions, such as facial expressions, self-assessed surveys, and biometric sensors, imply considerable intrusiveness on developers and tend to be used only during normal working periods. This article proposes to assess the feasibility of using social media posts (e.g., developers' posts on Twitter) to accurately determine the polarity of emotions of software developers over extended periods in a non-intrusive manner, allowing the identification of potentially abnormal periods of negative or positive sentiments of developers that may affect software development productivity or software quality. Our results suggested that Twitter data can serve as a valid source for accurately inferring the polarity of emotions. We evaluated 31 combinations of unsupervised lexicon-based techniques using a dataset with 79,029 public posts from Twitter from sixteen software developers, achieving a macro F1-Score of 0.745 and 76.8% of accuracy with the ensemble comprised of SentiStrength, Sentilex-PT, and LIWC2015_PT lexicons. Among other results, we found a statistically significant difference in tweets' polarities posted during working and non-working periods for 31.25% of the participants, suggesting that emotional polarity monitoring outside working hours could also be relevant. We also assessed the Big Five personality traits of the developers and preliminarily used them to ponder the polarities inferences. In this context, Openness, Conscientiousness, and Extraversion were frequently related to neutral and positive posts, while Neuroticism is associated with negative posts. Our results show that the proposed approach is accurate enough to constitute a simple and non-intrusive alternative to existing methods. Tools using this approach can be applied in real software development environments to support software team workers in making decisions to improve the software development process.

Keywords: Big Five; Lexicon; Personality trait; Sentiment analysis; Social media; Software engineering.

Grants and funding

This work was supported by the Grant CISUCUID/CEC/00326/2020, funded by the European Social Fund, through the Regional Operational Program Centro 2020. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.