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Sentiment analysis has various application scenarios in software engineering(SE), such as detecting developers' emotions in commit messages and identifyingtheir opinions on Q&A forums. However, commonly used out-of-the-box sentimentanalysis tools cannot obtain reliable results on SE tasks and themisunderstanding of technical jargon is demonstrated to be the main reason.Then, researchers have to utilize labeled SE-related texts to customizesentiment analysis for SE tasks via a variety of algorithms. However, thescarce labeled data can cover only very limited expressions and thus cannotguarantee the analysis quality. To address such a problem, we turn to theeasily available emoji usage data for help. More specifically, we employemotional emojis as noisy labels of sentiments and propose a representationlearning approach that uses both Tweets and GitHub posts containing emojis tolearn sentiment-aware representations for SE-related texts. These emoji-labeledposts can not only supply the technical jargon, but also incorporate moregeneral sentiment patterns shared across domains. They as well as labeled dataare used to learn the final sentiment classifier. Compared to the existingsentiment analysis methods used in SE, the proposed approach can achievesignificant improvement on representative benchmark datasets. By furthercontrast experiments, we find that the Tweets make a key contribution to thepower of our approach. This finding informs future research not to unilaterallypursue the domain-specific resource, but try to transform knowledge from theopen domain through ubiquitous signals such as emojis.

SEntiMoji: An Emoji-Powered Learning Approach for Sentiment Analysis in Software Engineering