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An initial procedure in text-as-data applications is text preprocessing. Oneof the typical steps, which can substantially facilitate computations, consistsin removing infrequent words believed to provide limited information about thecorpus. Despite popularity of vocabulary pruning, not many guidelines on how toimplement it are available in the literature. The aim of the paper is to fillthis gap by examining the effects of removing infrequent words for the qualityof topics estimated using Latent Dirichlet Allocation. The analysis is based onMonte Carlo experiments taking into account different criteria for infrequentterms removal and various evaluation metrics. The results indicate that pruningis beneficial and that the share of vocabulary which might be eliminated can bequite considerable.

Analysing the Impact of Removing Infrequent Words on Topic Quality in LDA Models