Diagnostics for topic modelling. The dubious joys of making quantitative decisions in a qualitative environment.

Diagnostics is a crucial component of any topic modelling application. However, available measures seldom offer indisputable and consistent solutions. We analyse the score distribution of a large set of intrinsic measures by varying two model inputs: text length and topic number. The first aim is to identify an ideal text length (or range of) by exploring per-length diagnostic distributions over the topic number. The second aim, once the optimal text length has been set, is to select the best model (or candidates) by comparing different specifications that include document metadata. We will also detect any conflict or ambivalence in the solutions produced by the different diagnostics.