Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of EASE 2021 |
| Subtitle of host publication | Evaluation and Assessment in Software Engineering |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 80-89 |
| Number of pages | 10 |
| ISBN (electronic) | 9781450390538 |
| Publication status | Published - 21 Jun 2021 |
| Event | 25th Evaluation and Assessment in Software Engineering Conference, EASE 2021 - Virtual, Online, Norway Duration: 21 Jun 2021 → 24 Jun 2021 |
Publication series
| Name | ACM International Conference Proceeding Series |
|---|
Abstract
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.
Keywords
- Machine Learning, Sentiment Analysis, Social Software Engineering, Systematic Literature Review
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Computer Networks and Communications
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Proceedings of EASE 2021: Evaluation and Assessment in Software Engineering. Association for Computing Machinery (ACM), 2021. p. 80-89 (ACM International Conference Proceeding Series).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Development and application of sentiment analysis tools in software engineering
T2 - 25th Evaluation and Assessment in Software Engineering Conference, EASE 2021
AU - Obaidi, Martin
AU - Klünder, Jil
PY - 2021/6/21
Y1 - 2021/6/21
N2 - 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.
AB - 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.
KW - Machine Learning
KW - Sentiment Analysis
KW - Social Software Engineering
KW - Systematic Literature Review
UR - http://www.scopus.com/inward/record.url?scp=85108915071&partnerID=8YFLogxK
U2 - 10.1145/3463274.3463328
DO - 10.1145/3463274.3463328
M3 - Conference contribution
AN - SCOPUS:85108915071
T3 - ACM International Conference Proceeding Series
SP - 80
EP - 89
BT - Proceedings of EASE 2021
PB - Association for Computing Machinery (ACM)
Y2 - 21 June 2021 through 24 June 2021
ER -