  Identifying feature requests and bug reports in user comments holds great
potential for development teams. However, automated mining of RE-related
information from social media and app stores is challenging since (1) about 70%
of user comments contain noisy, irrelevant information, (2) the amount of user
comments grows daily making manual analysis unfeasible, and (3) user comments
are written in different languages. Existing approaches build on traditional
machine learning (ML) and deep learning (DL), but fail to detect feature
requests and bug reports with high Recall and acceptable Precision which is
necessary for this task. In this paper, we investigate the potential of
transfer learning (TL) for the classification of user comments. Specifically,
we train both monolingual and multilingual BERT models and compare the
performance with state-of-the-art methods. We found that monolingual BERT
models outperform existing baseline methods in the classification of English
App Reviews as well as English and Italian Tweets. However, we also observed
that the application of heavyweight TL models does not necessarily lead to
better performance. In fact, our multilingual BERT models perform worse than
traditional ML methods.
