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

2023/11/24 by Victor Bystrov, Viktoriia Naboka‐Krell, Bystrov, Victor +5 · 1 citation
Computer Science · Social Sciences · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #Computational and Text Analysis Methods #FOS: Computer and information sciences #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2311.14505

openalex publication_date 2023/11/24 · openalex created_date 2023/11/28 · openalex updated_date 2026/07/28

Abstract

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

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• Mapping the unseen in practice: comparing latent Dirichlet allocation and BERTopic for navigating topic spaces

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