Achieving Reliable Sentiment Analysis in the Software Engineering Domain using BERT

Researchers have shown that sentiment analysis of software artifacts can potentially improve various software engineering tools, including API and library recommendation systems, code suggestion tools, and tools for improving communication among software developers. However, sentiment analysis techniques applied to software artifacts still have not yet yielded very high accuracy. Recent adaptations of sentiment analysis tools to the software domain have reported some improvements, but the f-measures for the positive and negative sentences still remain in the 0.4-0.64 range, which deters their practical usefulness for software engineering tools. In this paper, we explore the potential effectiveness of customizing BERT, a language representation model, which has recently achieved very good results on various Natural Language Processing tasks on English texts, for the task of sentiment analysis of software artifacts. We describe our application of BERT to analyzing sentiments of sentences in Stack Overflow posts and compare the impact of a BERT sentiment classifier to state-of-the-art sentiment analysis techniques when used on a domain-specific data set created from Stack Overflow posts. We also investigate how the performance of sentiment analysis changes when using a much (3 times) larger data set than previous studies. Our results show that the BERT classifier achieves reliable performance for sentiment analysis of software engineering texts. BERT combined with the larger data set achieves an overall f-measure of 0.87, with the f-measures for the negative and positive sentences reaching 0.91 and 0.78 respectively, a significant improvement over the state-of-the-art.