This paper presents the results of a replication experiment for automatic irony detection in Dutch social media text, investigating both a feature-based SVM classifier, as was done by Van Hee et al. (2017) and a transformer-based approach. In addition to building a baseline model, an important goal of this research is to explore the implementation of commonsense knowledge in the form of implicit sentiment, as we strongly believe that commonsense and connotative knowledge are essential to the identification of irony and implicit meaning in tweets. We show promising results and how the presented approach can provide a solid baseline and serve as a staging ground to build on in future experiments for irony detection in Dutch.