  Sentiment analysis, a popular technique for opinion mining, has been used by
the software engineering research community for tasks such as assessing app
reviews, developer emotions in issue trackers and developer opinions on APIs.
Past research indicates that state-of-the-art sentiment analysis techniques
have poor performance on SE data. This is because sentiment analysis tools are
often designed to work on non-technical documents such as movie reviews. In
this study, we attempt to solve the issues with existing sentiment analysis
techniques for SE texts by proposing a hierarchical model based on
convolutional neural networks (CNN) and long short-term memory (LSTM) trained
on top of pre-trained word vectors. We assessed our model's performance and
reliability by comparing it with a number of frequently used sentiment analysis
tools on five gold standard datasets. Our results show that our model pushes
the state of the art further on all datasets in terms of accuracy. We also show
that it is possible to get better accuracy after labelling a small sample of
the dataset and re-training our model rather than using an unsupervised
classifier.
