Agile social media analysis involves building bespoke, one-off classification pipelines tailored to the analysis of specific datasets. In this study we investigate how the DUALIST architecture can be optimised for agile social media analysis. We evaluate several semi-supervised learning algorithms in conjunction with a Na ̈ıve Bayes model, and show how these modifications can improve the performance of bespoke classifiers for a variety of tasks on a large range of datasets.