  Stance detection is identifying expressed beliefs in a document. While
researchers widely use sentiment analysis for this, recent research
demonstrates that sentiment and stance are distinct. This paper advances text
analysis methods by precisely defining stance detection and presenting three
distinct approaches: supervised classification, natural language inference, and
in-context learning with generative language models. I discuss how document
context and trade-offs between resources and workload should inform your
methods. For all three approaches I provide guidance on application and
validation techniques, as well as coding tutorials for implementation. Finally,
I demonstrate how newer classification approaches can replicate supervised
classifiers.
