Deriving business insights from user-generated content (UGC) is a widely investigated phenomenon in information systems (IS) research. Due to its unstructured nature and technical constraints, UGC is still underutilized as a data source in research and practice. Using recent advancements in machine learning research, especially large language models (LLMs), IS researchers can possibly derive these insights more effectively. To guide and further understand the usage of these techniques, we develop a taxonomy that provides an overview of business insights derived from UGC. The taxonomy helps both practitioners and researchers identify, design, compare and evaluate the use of UGC in this IS context. Finally, we showcase an LLM-supported demo application that derives novel business insights and apply the taxonomy to it. In doing so, we show exemplary how LLMs can be used to develop new or extend existing NLP applications in the realm of IS.
Weingart, Pablo; Wambsganss, Thiemo; and Soellner, Matthias, "A taxonomy for deriving business insights from user-generated content" (2023). ECIS 2023 Research Papers. 401.