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

## TL;DR

This paper introduces SEntiMoji, a novel sentiment analysis method for software engineering that leverages emoji-labeled social media data to improve accuracy over traditional tools, especially in technical contexts.

## Contribution

The paper proposes using emoji-based noisy labels from social media to enhance sentiment representation learning in software engineering texts, addressing data scarcity and jargon issues.

## Key findings

- Emoji-labeled posts improve sentiment classification accuracy.
- Tweets contribute significantly to the model's effectiveness.
- The approach outperforms existing SE sentiment analysis methods.

## 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.