  With the rise of social media like Twitter and of software distribution
platforms like app stores, users got various ways to express their opinion
about software products. Popular software vendors get user feedback
thousandfold per day. Research has shown that such feedback contains valuable
information for software development teams such as problem reports or feature
and support inquires. Since the manual analysis of user feedback is cumbersome
and hard to manage many researchers and tool vendors suggested to use automated
analyses based on traditional supervised machine learning approaches. In this
work, we compare the results of traditional machine learning and deep learning
in classifying user feedback in English and Italian into problem reports,
inquiries, and irrelevant. Our results show that using traditional machine
learning, we can still achieve comparable results to deep learning, although we
collected thousands of labels.
