activity
20172019
most citedSEntiMoji: An Emoji-Powered Learning Approach for Sentiment Analysis in Software Engineering

49 citations · 113 across the 7 of their papers we have counts for

collaborators

8 papers

cs.SE201949 cited

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

Zhenpeng Chen, Yanbin Cao, Xuan Lu +2

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 f…

cs.SI20192 cited

A Systematic Analysis of Fine-Grained Human Mobility Prediction with On-Device Contextual Data

Huoran Li

User mobility prediction is widely considered to be helpful for various sorts of location based services on mobile devices. A large amount of studies have explored different algori…

cs.CY20188 cited

A First Look at Emoji Usage on GitHub: An Empirical Study

Xuan Lu, Yanbin Cao, Zhenpeng Chen +1

Emoji is becoming a ubiquitous language and gaining worldwide popularity in recent years including the field of software engineering (SE). As nonverbal cues, emojis are widely used…

cs.IR2018

Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification

Zhenpeng Chen, Sheng Shen, Ziniu Hu +3

Sentiment classification typically relies on a large amount of labeled data. In practice, the availability of labels is highly imbalanced among different languages, e.g., more Engl…

cs.SE20172 cited

Mining Device-Specific Apps Usage Patterns from Large-Scale Android Users

Huoran Li, Xuan Lu

When smartphones, applications (a.k.a, apps), and app stores have been widely adopted by the billions, an interesting debate emerges: whether and to what extent do device models in…

cs.SE2017

PRADA Applicability in Industrial Practice

Xuan Lu

The proliferation of Android devices brings the fragmentation problem. Selecting and prioritizing major device models are critical for mobile app developers to select testbeds and…