Details
| Original language | English |
|---|---|
| Article number | 107018 |
| Journal | Information and Software Technology |
| Volume | 151 |
| Early online date | 22 Jul 2022 |
| Publication status | Published - Nov 2022 |
Abstract
Keywords
- Social software engineering, Sentiment analysis, Machine learning, Systematic mapping study, Systematic literature review
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Computer Science Applications
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In: Information and Software Technology, Vol. 151, 107018, 11.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Sentiment analysis tools in software engineering
T2 - A systematic mapping study
AU - Obaidi, Martin
AU - Nagel, Lukas
AU - Specht, Alexander
AU - Klünder, Jil
N1 - Funding Information: This work was supported by the Leibniz Young Investigator Grant, Germany and is part of the project ComContA, 2020–2022.
PY - 2022/11
Y1 - 2022/11
N2 - Context: Software development is a collaborative task. Previous research has shown social aspects within development teams to be highly relevant for the success of software projects. A team’s mood has been proven to be particularly important. It is paramount for project managers to be aware of negative moods within their teams, as such awareness enables them to intervene. Sentiment analysis tools offer a way to determine the mood of a team based on textual communication. Objective: We aim to help developers or stakeholders in their choice of sentiment analysis tools for their specific purpose. Therefore, we conducted a systematic mapping study (SMS). Methods: We present the results of our SMS of sentiment analysis tools developed for or applied in the context of software engineering (SE). Our results summarize insights from 106 papers with respect to (1) the application domain, (2) the purpose, (3) the used data sets, (4) the approaches for developing sentiment analysis tools, (5) the usage of already existing tools, and (6) the difficulties researchers face. We analyzed in more detail which tools and approaches perform how in terms of their performance. Results: According to our results, sentiment analysis is frequently applied to open-source software projects, and most approaches are neural networks or support-vector machines. The best performing approach in our analysis is neural networks and the best tool is BERT. Despite the frequent use of sentiment analysis in SE, there are open issues, e.g. regarding the identification of irony or sarcasm, pointing to future research directions. Conclusion: We conducted an SMS to gain an overview of the current state of sentiment analysis in order to help developers or stakeholders in this matter. Our results include interesting findings e.g. on the used tools and their difficulties. We present several suggestions on how to solve these identified problems.
AB - Context: Software development is a collaborative task. Previous research has shown social aspects within development teams to be highly relevant for the success of software projects. A team’s mood has been proven to be particularly important. It is paramount for project managers to be aware of negative moods within their teams, as such awareness enables them to intervene. Sentiment analysis tools offer a way to determine the mood of a team based on textual communication. Objective: We aim to help developers or stakeholders in their choice of sentiment analysis tools for their specific purpose. Therefore, we conducted a systematic mapping study (SMS). Methods: We present the results of our SMS of sentiment analysis tools developed for or applied in the context of software engineering (SE). Our results summarize insights from 106 papers with respect to (1) the application domain, (2) the purpose, (3) the used data sets, (4) the approaches for developing sentiment analysis tools, (5) the usage of already existing tools, and (6) the difficulties researchers face. We analyzed in more detail which tools and approaches perform how in terms of their performance. Results: According to our results, sentiment analysis is frequently applied to open-source software projects, and most approaches are neural networks or support-vector machines. The best performing approach in our analysis is neural networks and the best tool is BERT. Despite the frequent use of sentiment analysis in SE, there are open issues, e.g. regarding the identification of irony or sarcasm, pointing to future research directions. Conclusion: We conducted an SMS to gain an overview of the current state of sentiment analysis in order to help developers or stakeholders in this matter. Our results include interesting findings e.g. on the used tools and their difficulties. We present several suggestions on how to solve these identified problems.
KW - Social software engineering
KW - Sentiment analysis
KW - Machine learning
KW - Systematic mapping study
KW - Systematic literature review
UR - http://www.scopus.com/inward/record.url?scp=85135527315&partnerID=8YFLogxK
U2 - 10.1016/j.infsof.2022.107018
DO - 10.1016/j.infsof.2022.107018
M3 - Article
VL - 151
JO - Information and Software Technology
JF - Information and Software Technology
SN - 0950-5849
M1 - 107018
ER -