一句话：指出现有通用情感分析工具在软件开发文本上误分类技术术语，影响实证研究结论效度。
论文关注情感分析在软件开发者情绪研究中的应用。随着挖掘软件仓库和众包内容成为研究开发者情绪的手段，经验软件工程研究多直接使用现成情感分析工具。然而这些工具在非技术领域和通用社交媒体上训练，容易误判技术术语和问题报告。Jongeling 等人的工作表明，工具选择会影响实证研究的结论效度：它们既与开发者交流渠道的人工标注不一致，彼此之间也不一致。因此，面向软件开发的情感极性检测需要专门处理技术语境，以提升标注一致性和研究可靠性。
The role of sentiment analysis is increasingly emerging to study software developers' emotions by mining crowd-generated content within software repositories and information sources. With a few notable exceptions [1][5], empirical software engineering studies have exploited off-the-shelf sentiment analysis tools. However, such tools have been trained on non-technical domains and general-purpose social media, thus resulting in misclassifications of technical jargon and problem reports [2][4]. In particular, Jongeling et al. [2] show how the choice of the sentiment analysis tool may impact the conclusion validity of empirical studies because not only these tools do not agree with human annotation of developers' communication channels, but they also disagree among themselves.
DOI 原文 ·
@inproceedings{paperbot4540,
title = {Sentiment polarity detection for software development},
author = {Fabio Calefato and Filippo Lanubile and Federico Maiorano and Nicole Novielli},
booktitle = {ICSE},
year = {2018},
doi = {10.1145/3180155.3182519}
}