What's in a name? The effect of named entities on topic modelling interpretability

Topic Modelling has established itself as one of the major text-as-data methodologies within the social sciences in general, and in communications science, in particular. The core strength of TM approaches is the fact that it is essentially a 2-in-1 method, as it both generates clusters into which the texts may fall as well as classifies the texts according to the clusters. Previous research has pointed out that pre-processing text corpora is as much a part of text analysis as the latter stages. Named Entity Recognition, however, is not often thought of when pre-processing texts for analysis and has thus far not received much attention in relation to the TM pipeline. If simply retaining or removing stop words can produce different interpretations of the outcomes of TM, retaining or removing NEs also has consequences on outcomes and interpretations. The current paper analyses the effects that removing/retaining NEs has on the interpretability of topic models. Both model statistics and human validation are used to address this issue. The results show differences in topics models trained on corpora with and without NEs. TMs trained on corpora where NEs are removed exhibit different structural characteristics, and, more importantly, are perceived differently by human coders. We attempt to formulate recommendations regarding the pre-processing of NEs in TM applications.