  User feedback is becoming an increasingly important source of information for
requirements engineering, user interface design, and software engineering in
general. Nowadays, user feedback is largely available and easily accessible in
social media, product forums, or app stores. Over the last decade, research has
shown that user feedback can help software teams: a) better understand how
users are actually using specific product features and components, b) faster
identify, reproduce, and fix defects, and b) get inspirations for improvements
or new features. However, to tap the full potential of feedback, there are two
main challenges that need to be solved. First, software vendors must cope with
a large quantity of feedback data, which is hard to manage manually. Second,
vendors must also cope with a varying quality of feedback as some items might
be uninformative, repetitive, or simply wrong. This chapter summarises and
pipelines various data mining, machine learning, and natural language
processing techniques, including recent Large Language Models, to cope with the
quantity and quality challenges. We guide researchers and practitioners through
implementing effective, actionable analysis of user feedback for software and
requirements engineering.
