Development and Application of Sentiment Analysis Tools in Software Engineering: A Systematic Literature Review
Not on the map. Why?
Abstract (arXiv)
Software development is a collaborative task and, hence, involves different persons. Research has shown the relevance of social aspects in the development team for a successful and satisfying project closure. Especially the mood of a team has been proven to be of particular importance. Thus, project managers or project leaders want to be aware of situations in which negative mood is present to allow for interventions. So-called sentiment analysis tools offer a way to determine the mood based on text-based communication. In this paper, we present the results of a systematic literature review of sentiment analysis tools developed for or applied in the context of software engineering. Our results summarize insights from 80 papers with respect to (1) the application domain, (2) the purpose, (3) the used data sets, (4) the approaches for developing sentiment analysis tools and (5) the difficulties researchers face when applying sentiment analysis in the context of software projects. According to our results, sentiment analysis is frequently applied to open-source software projects, and most tools are based on support-vector machines. Despite the frequent use of sentiment analysis in software engineering, there are open issues, e.g., regarding the identification of irony or sarcasm, pointing to future research directions.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 May 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 0; 2026: 2.
3 papers.
- 2022An Empirical Study of IoT Security Aspects at Sentence-Level in Developer Textual Discussionscited by 3
- 2026Machine Learning Models for the Early Detection of Burnout in Software Engineering: a Systematic Literature Reviewcited by 0
- 2026Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go?cited by 0
- 2026Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go?15 Apr 2026
- 2026Machine Learning Models for the Early Detection of Burnout in Software Engineering: a Systematic Literature Review24 Mar 2026
- 2022An Empirical Study of IoT Security Aspects at Sentence-Level in Developer Textual Discussions7 Jun 2022