Sentiment analysis tools in software engineering: A systematic mapping study
🤖 AI Summary
This study addresses the growing need for emotion recognition in software development teams by systematically investigating the suitability of sentiment analysis tools for software engineering contexts. Following the PRISMA guidelines, we conducted a systematic mapping study, screening and analyzing 138 primary studies to establish— for the first time—the first comprehensive taxonomy of sentiment analysis tools spanning the entire software engineering lifecycle. The taxonomy identifies seven tool categories and five prototypical application scenarios (e.g., requirements engineering, code review), revealing critical gaps in scenario-specific adaptability, empirical validation, and cross-lifecycle support. Our key contributions are: (1) a novel, practice-oriented classification framework for sentiment analysis tools in software engineering; (2) clearly defined evaluation dimensions and applicability boundaries; and (3) an actionable research roadmap to guide both researchers and practitioners. This work provides theoretical foundations and practical guidance for project managers selecting and deploying sentiment analysis tools effectively.
Application Category
Problem
Research questions and friction points this paper is trying to address.
Sentiment analysis tools for software engineering
Identifying team mood via textual communication
Comparing tool performance in sentiment analysis
Innovation
Methods, ideas, or system contributions that make the work stand out.
Systematic mapping study methodology
Neural networks for sentiment analysis
BERT as top-performing tool
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