Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Software Engineering 2023 |
| Untertitel | Fachtagung des GI-Fachbereichs Softwaretechnik |
| Herausgeber/-innen | Gregor Engels, Regina Hebig, Matthias Tichy |
| Herausgeber (Verlag) | Gesellschaft fur Informatik (GI) |
| Seiten | 67-68 |
| Seitenumfang | 2 |
| ISBN (elektronisch) | 9783885797265 |
| Publikationsstatus | Veröffentlicht - 2023 |
| Veranstaltung | 2023 Fachtagung des GI-Fachbereichs Softwaretechnik, Software Engineering 2023 - 2023 Conference of the GI Software Engineering Division, Software Engineering 2023 - Paderborn, Deutschland Dauer: 20 Feb. 2023 → 24 Feb. 2023 |
Publikationsreihe
| Name | Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI) |
|---|---|
| Band | P-332 |
| ISSN (Print) | 1617-5468 |
Abstract
Social aspects (e.g., the sentiment of developers) are important for software development. In order to automatically analyze sentiments, sentiment analysis tools use machine learning methods that require data sets labeled according to emotion or polarity. As these labeled data sets strongly influence the tools' accuracy, we investigate whether the labels match developers' perceptions. For this purpose, we conducted an international survey with 94 participants who labeled 100 statements. We compare the median as well as every single participant's perception with the labels. The results show that the median perception of all participants coincides with the predefined labels for 62.5% of the statements, and that the difference between the single participant's ratings and the labels is even worse. This summary refers to the paper with the title “On the subjectivity of emotions in software projects: How reliable are pre-labeled data sets for sentiment analysis?” [He22b]. It was published in the Journal of Systems and Software (JSS) in 2022 peer-reviewed.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Angewandte Informatik
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
Software Engineering 2023 : Fachtagung des GI-Fachbereichs Softwaretechnik. Hrsg. / Gregor Engels; Regina Hebig; Matthias Tichy. Gesellschaft fur Informatik (GI), 2023. S. 67-68 (Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI); Band P-332).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - On the Subjectivity of Emotions in Software Projects
T2 - 2023 Fachtagung des GI-Fachbereichs Softwaretechnik, Software Engineering 2023 - 2023 Conference of the GI Software Engineering Division, Software Engineering 2023
AU - Herrmann, Marc
AU - Obaidi, Martin
AU - Chazette, Larissa
AU - Klünder, Jil
PY - 2023
Y1 - 2023
N2 - Social aspects (e.g., the sentiment of developers) are important for software development. In order to automatically analyze sentiments, sentiment analysis tools use machine learning methods that require data sets labeled according to emotion or polarity. As these labeled data sets strongly influence the tools' accuracy, we investigate whether the labels match developers' perceptions. For this purpose, we conducted an international survey with 94 participants who labeled 100 statements. We compare the median as well as every single participant's perception with the labels. The results show that the median perception of all participants coincides with the predefined labels for 62.5% of the statements, and that the difference between the single participant's ratings and the labels is even worse. This summary refers to the paper with the title “On the subjectivity of emotions in software projects: How reliable are pre-labeled data sets for sentiment analysis?” [He22b]. It was published in the Journal of Systems and Software (JSS) in 2022 peer-reviewed.
AB - Social aspects (e.g., the sentiment of developers) are important for software development. In order to automatically analyze sentiments, sentiment analysis tools use machine learning methods that require data sets labeled according to emotion or polarity. As these labeled data sets strongly influence the tools' accuracy, we investigate whether the labels match developers' perceptions. For this purpose, we conducted an international survey with 94 participants who labeled 100 statements. We compare the median as well as every single participant's perception with the labels. The results show that the median perception of all participants coincides with the predefined labels for 62.5% of the statements, and that the difference between the single participant's ratings and the labels is even worse. This summary refers to the paper with the title “On the subjectivity of emotions in software projects: How reliable are pre-labeled data sets for sentiment analysis?” [He22b]. It was published in the Journal of Systems and Software (JSS) in 2022 peer-reviewed.
KW - communication
KW - development team
KW - polarity
KW - Sentiment analysis
KW - software projects
UR - http://www.scopus.com/inward/record.url?scp=85150068781&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85150068781
T3 - Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)
SP - 67
EP - 68
BT - Software Engineering 2023
A2 - Engels, Gregor
A2 - Hebig, Regina
A2 - Tichy, Matthias
PB - Gesellschaft fur Informatik (GI)
Y2 - 20 February 2023 through 24 February 2023
ER -