Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

10-2020

Abstract

Extensive research has been conducted on sentiment analysis for software engineering (SA4SE). Researchers have invested much effort in developing customized tools (e.g., SentiStrength-SE, SentiCR) to classify the sentiment polarity for Software Engineering (SE) specific contents (e.g., discussions in Stack Overflow and code review comments). Even so, there is still much room for improvement. Recently, pre-trained Transformer-based models (e.g., BERT, XLNet) have brought considerable breakthroughs in the field of natural language processing (NLP). In this work, we conducted a systematic evaluation of five existing SA4SE tools and variants of four state-of-the-art pre-trained Transformer-based models on six SE datasets. Our work is the first to fine-tune pre-trained Transformer-based models for the SA4SE task. Empirically, across all six datasets, our fine-tuned pre-trained Transformer-based models outperform the existing SA4SE tools by 6.5-35.6% in terms of macro/micro-averaged F1 scores.

Keywords

Natural Language Processing, Pre-trained Models, Sentiment Analysis, Software Mining

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

2020 36th IEEE International Conference on Software Maintenance and Evolution (ICSME): Sep 27 - Oct 3, Adelaide, Australia: Proceedings

First Page

70

Last Page

80

ISBN

9781728156194

Identifier

10.1109/ICSME46990.2020.00017

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/ICSME46990.2020.00017

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