A German Gold-Standard Dataset for Sentiment Analysis in Software Engineering

TL;DR

This paper introduces a new German sentiment analysis dataset for software engineering, enabling better emotion detection in German developer communications and addressing the lack of domain-specific tools.

Contribution

The work provides the first German gold-standard dataset for software engineering sentiment analysis, with high annotation reliability and potential to improve domain-specific tools.

Findings

High interrater agreement in annotations

Existing tools lack domain-specific accuracy

Dataset supports future sentiment analysis research

Abstract

Sentiment analysis is an essential technique for investigating the emotional climate within developer teams, contributing to both team productivity and project success. Existing sentiment analysis tools in software engineering primarily rely on English or non-German gold-standard datasets. To address this gap, our work introduces a German dataset of 5,949 unique developer statements, extracted from the German developer forum Android-Hilfe.de. Each statement was annotated with one of six basic emotions, based on the emotion model by Shaver et al., by four German-speaking computer science students. Evaluation of the annotation process showed high interrater agreement and reliability. These results indicate that the dataset is sufficiently valid and robust to support sentiment analysis in the German-speaking software engineering community. Evaluation with existing German sentiment analysis tools confirms the lack of domain-specific solutions for software engineering. We also discuss approaches to optimize annotation and present further use cases for the dataset.