A Comparative Study of Topic Modelling Approaches for User-generated Point of Interest Data

Abstract

This study aims to enhance urban planning and management by harnessing the power of machine learning (ML) and
big data. We focus on Urban Functional Zones (UFZs), the fundamental units for human socio-economic activities. Our
methodology involves compiling Point of Interest (POI) data from various sources for comprehensive analysis. We
employ various topic modeling approaches such as Latent Dirichlet Allocation (LDA), Latent Semantic Index (LSI),
Hierarchical Dirichlet Process (HDP), and Top2Vec. Our principal results reveal significant differences in the
performance and coherence of these models on short text documents. Consequently, our major conclusion is
identifying the better-performing topic model for classifying UFZs from POI data. We also explore four text
preprocessing steps to optimize the performance of the topic models. This study contributes to the field by providing
a nuanced understanding of UFZs, paving the way for future data-driven urban planning and management.