Sentiment Analysis of ML Projects: Bridging Emotional Intelligence and Code Quality

• URL: http://arxiv.org/abs/2409.17885v1
• Date: Thu, 26 Sep 2024 14:34:01 GMT
• Title: Sentiment Analysis of ML Projects: Bridging Emotional Intelligence and Code Quality
• Authors: Md Shoaib Ahmed, Dongyoung Park, Nasir U. Eisty,
• Abstract summary: This study explores the relationship between sentiment analysis (SA) and code quality within machine learning (ML) projects. By integrating a comprehensive dataset of popular ML repositories, this analysis applies a blend of rule-based, machine learning, and hybrid sentiment analysis methodologies. Findings show that positive sentiments among developers are strongly associated with superior code quality manifested through reduced bugs and lower incidence of code smells.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study explores the intricate relationship between sentiment analysis (SA) and code quality within machine learning (ML) projects, illustrating how the emotional dynamics of developers affect the technical and functional attributes of software projects. Recognizing the vital role of developer sentiments, this research employs advanced sentiment analysis techniques to scrutinize affective states from textual interactions such as code comments, commit messages, and issue discussions within high-profile ML projects. By integrating a comprehensive dataset of popular ML repositories, this analysis applies a blend of rule-based, machine learning, and hybrid sentiment analysis methodologies to systematically quantify sentiment scores. The emotional valence expressed by developers is then correlated with a spectrum of code quality indicators, including the prevalence of bugs, vulnerabilities, security hotspots, code smells, and duplication instances. Findings from this study distinctly illustrate that positive sentiments among developers are strongly associated with superior code quality metrics manifested through reduced bugs and lower incidence of code smells. This relationship underscores the importance of fostering positive emotional environments to enhance productivity and code craftsmanship. Conversely, the analysis reveals that negative sentiments correlate with an uptick in code issues, particularly increased duplication and heightened security risks, pointing to the detrimental effects of adverse emotional conditions on project health.

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