Paper Info
Title
SEntiMoji - an emoji-powered learning approach for sentiment analysis in software engineering.
Abstract
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.
Year
DOI
Venue
2019
10.1145/3338906.3338977
ESEC/SIGSOFT FSE
Keywords
DocType
ISBN
Emoji,Sentiment analysis,Software engineering
Conference
978-1-4503-5572-8
Citations 
PageRank 
References 
5
0.38
0
Authors
5
Name
Order
Citations
PageRank
Zhenpeng Chen1356.65
Zhenpeng Chen2356.65
Yanbin Cao3141.82
xuan lu416212.01
Qiaozhu Mei54395207.09