ABSTRACT
The paper presents an analysis of developer commit logs for GitHub projects. In particular, developer sentiment in commits is analyzed across 28,466 projects within a seven year time frame. We use the Boa infrastructure's online query system to generate commit logs as well as files that were changed during the commit. We analyze the commits in three categories: large, medium, and small based on the number of commits using a sentiment analysis tool. In addition, we also group the data based on the day of week the commit was made and map the sentiment to the file change history to determine if there was any correlation. Although a majority of the sentiment was neutral, the negative sentiment was about 10% more than the positive sentiment overall. Tuesdays seem to have the most negative sentiment overall. In addition, we do find a strong correlation between the number of files changed and the sentiment expressed by the commits the files were part of. Future work and implications of these results are discussed.
- E. Guzman, D. Azócar, and Y. Li, "Sentiment analysis of commit comments in GitHub: an empirical study," in Proceedings of the 11th Working Conference on Mining Software Repositories, 2014, pp. 352--355. Google ScholarDigital Library
- P. Tourani, Y. Jiang, and B. Adams, "Monitoring sentiment in open source mailing lists-exploratory study on the apache ecosystem," in Proceedings of the 2014 Conference of the Center for Advanced Studies on Collaborative Research (CASCON), Toronto, ON, Canada, 2014, pp. 74--95.Google Scholar
- A. Murgia, P. Tourani, B. Adams, and M. Ortu, "Do developers feel emotions? an exploratory analysis of emotions in software artifacts," in Proceedings of the 11th Working Conference on Mining Software Repositories, 2014, pp. 262--271. Google ScholarDigital Library
- R. Jongeling, S. Datta, and A. Serebrenik, "Choosing your weapons: On sentiment analysis tools for software engineering research," in Software Maintenance and Evolution (ICSME), 2015 IEEE International Conference on, 2015, pp. 531--535. Google ScholarDigital Library
- R. Dyer, H. A. Nguyen, H. Rajan, and T. N. Nguyen, "Boa: A language and infrastructure for analyzing ultra-large-scale software repositories," in Proceedings of the 2013 International Conference on Software Engineering, 2013, pp. 422--431. Google ScholarDigital Library
- M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, and A. Kappas, "Sentiment strength detection in short informal text," J. Am. Soc. Inf. Sci. Technol., vol. 61, no. 12, pp. 2544--2558, Dec. 2010. Google ScholarDigital Library
- N. Friedrich, T. D. Bowman, W. G. Stock, and S. Haustein, "Adapting sentiment analysis for tweets linking to scientific papers," ArXiv Prepr. ArXiv150701967, 2015.Google Scholar
Index Terms
- Analyzing developer sentiment in commit logs
Recommendations
Joint sentiment/topic model for sentiment analysis
CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge managementSentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet ...
Social sentiment sensor: a visualization system for topic detection and topic sentiment analysis on microblog
As a new form of social media, microblogging provides platform sharing, wherein users can share their feelings and ideas on certain topics. Bursty topics from microblogs are the results of the emerging issues that instantly attract more followers and ...
Sentence compression for aspect-based sentiment analysis
Sentiment analysis, which addresses the computational treatment of opinion, sentiment, and subjectivity in text, has received considerable attention in recent years. In contrast to the traditional coarse-grained sentiment analysis tasks, such as ...
Comments