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Assessing place experiences in Luton and Darlington on Twitter with topic modelling and AI-generated lexicons

Research Output:
Contribution to journal
Article
 Peer-review

Abstract

Purpose: The purpose of this paper is to examine and compare the in situ place experiences of people in Luton and Darlington. Design/methodology/approach: The study used 109,998 geotagged tweets from Luton and Darlington between 2020 and 2022 and conducted topic modelling using latent Dirichlet allocation. Lexicons were created using GPT-4 to evaluate the eight dimensions of place experience for each topic. Findings: The study found that Darlington had higher counts in the sensorial, behavioural, designed and mundane dimensions of place experience than Luton. Conversely, Luton had a higher prevalence of the affective and intellectual dimensions, attributed to political and faith-related tweets. Originality/value: The study introduces a novel approach that uses AI-generated lexicons for place experience. These lexicons cover four facets, two intentions and two intensities of place experience, enabling detection of words from any domain. This approach can be useful not only for town and destination brand managers but also for researchers in any field.

Publication Information

Output type

Research Output:
Contribution to journal
Article
 Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 49-73 (25 pages)

Journal (Volume, Issue Number)

Journal of Place Management and Development (Volume 17, Issue 1)

Publication milestones

  • Published - 28/07/2023

Publication status

Published - 28/07/2023

ISSN

1753-8335

Publication IDs

  • handle.net: 10547/626196
  • Scopus: 85165877576

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