Logo des Repositoriums
 
Konferenzbeitrag

On the Subjectivity of Emotions in Software Projects: How Reliable are Pre-Labeled Data Sets for Sentiment Analysis? (Summary)

Vorschaubild nicht verfügbar

Volltext URI

Dokumententyp

Text/Conference Paper

Zusatzinformation

Datum

2023

Zeitschriftentitel

ISSN der Zeitschrift

Bandtitel

Verlag

Gesellschaft für Informatik e.V.

Zusammenfassung

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.

Beschreibung

Herrmann, Marc; Obaidi, Martin; Chazette, Larissa; Klünder, Jil (2023): On the Subjectivity of Emotions in Software Projects: How Reliable are Pre-Labeled Data Sets for Sentiment Analysis? (Summary). Software Engineering 2023. Bonn: Gesellschaft für Informatik e.V.. PISSN: 1617-5468. ISBN: 978-3-88579-726-5. pp. 67-68. Wissenschaftliches Hauptprogramm. Paderborn. 20.–24. Februar 2023

Zitierform

DOI

Tags