一句话：利用 emoji 作为噪声标签，从推文与 GitHub 帖子学习情感表示，提升软件工程文本情感分析效果。
软件工程中的情感分析可用于检测提交信息中的开发者情绪、识别问答社区中的观点等，但现成情感分析工具常因误解技术术语而在 SE 任务上表现不可靠。由于标注 SE 文本稀缺且覆盖表达有限，本文提出 SEntiMoji，将情感 emoji 视为噪声标签，利用同时包含 emoji 的推文和 GitHub 帖子进行表示学习，为 SE 相关文本学习情感感知表示。这些 emoji 标注帖子既能补充技术术语，也能引入跨领域的通用情感模式。最终表示与标注数据一起用于训练情感分类器。在代表性基准数据集上，该方法相比现有 SE 情感分析方法取得显著提升；对比实验进一步表明，推文数据对方法能力具有关键贡献。
Sentiment analysis has various application scenarios in software engineering (SE), such as detecting developers' emotions in commit messages and identifying their opinions on Q&A forums. However, commonly used out-of-the-box sentiment analysis tools cannot obtain reliable results on SE tasks and the misunderstanding of technical jargon is demonstrated to be the main reason. Then, researchers have to utilize labeled SE-related texts to customize sentiment analysis for SE tasks via a variety of algorithms. However, the scarce labeled data can cover only very limited expressions and thus cannot guarantee the analysis quality. To address such a problem, we turn to the easily available emoji usage data for help. More specifically, we employ emotional emojis as noisy labels of sentiments and propose a representation learning approach that uses both Tweets and GitHub posts containing emojis to learn sentiment-aware representations for SE-related texts. These emoji-labeled posts can not only supply the technical jargon, but also incorporate more general sentiment patterns shared across domains. They as well as labeled data are used to learn the final sentiment classifier. Compared to the existing sentiment analysis methods used in SE, the proposed approach can achieve significant improvement on representative benchmark datasets. By further contrast experiments, we find that the Tweets make a key contribution to the power of our approach. This finding informs future research not to unilaterally pursue the domain-specific resource, but try to transform knowledge from the open domain through ubiquitous signals such as emojis.
@inproceedings{paperbot5105,
title = {SEntiMoji: an emoji-powered learning approach for sentiment analysis in software engineering},
author = {Zhenpeng Chen and Yanbin Cao and Xuan Lu and Qiaozhu Mei and Xuanzhe Liu},
booktitle = {FSE},
year = {2019},
doi = {10.1145/3338906.3338977}
}