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

TL;DR

This paper investigates how removing infrequent words affects the quality of topics generated by LDA models, providing guidelines based on Monte Carlo experiments.

Contribution

It offers a systematic analysis of vocabulary pruning effects on LDA topic quality, filling a gap in existing preprocessing guidelines.

Findings

Pruning infrequent words improves topic quality.

A significant portion of vocabulary can be safely removed.

Guidelines for effective vocabulary pruning are proposed.

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.