  Data-driven requirements engineering leverages the abundance of openly
accessible and crowdsourced information on the web. By incorporating user
feedback provided about a software product, such as reviews in mobile app
stores, these approaches facilitate the identification of issues, bug fixes,
and implementation of change requests. However, relying solely on user feedback
about a software product limits the possibility of eliciting all requirements,
as users may not always have a clear understanding of their exact needs from
the software, despite their wealth of experience with the problem, event, or
challenges they encounter and use the software to assist them. In this study,
we propose a shift in requirements elicitation, focusing on gathering feedback
related to the problem itself rather than relying solely on feedback about the
software product. We conducted a case study on student requirements during the
COVID-19 pandemic in a higher education institution. We gathered their
communications from Reddit during the pandemic and employed multiple
machine-learning and natural language processing techniques to identify
requirement sentences. We achieved the F-score of 0.79 using Naive Bayes with
TF-IDF when benchmarking multiple techniques. The results lead us to believe
that mining requirements from communication about a problem are feasible. While
we present the preliminary results, we envision a future where these
requirements complement conventionally elicited requirements and help to close
the requirements gap.
