License: 
Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2023.81
URN: urn:nbn:de:0030-drops-189769
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18976/
Wang, Boyu ;
Crooks, Andrew
Agent-Based Modeling of Consumer Choice by Utilizing Crowdsourced Data and Deep Learning (Short Paper)
Abstract
People’s opinions are one of the defining factors that turn spaces into meaningful places. Online platforms such as Yelp allow users to publish their reviews on businesses. To understand reviewers' opinion formation processes and the emergent patterns of published opinions, we utilize natural language processing (NLP) techniques especially that of aspect-based sentiment analysis methods (a deep learning approach) on a geographically explicit Yelp dataset to extract and categorize reviewers' opinion aspects on places within urban areas. Such data is then used as a basis to inform an agent-based model, where consumers' (i.e., agents') choices are based on their characteristics and preferences. The results show the emergent patterns of reviewers' opinions and the influence of these opinions on others. As such this work demonstrates how using deep learning techniques on geospatial data can help advance our understanding of place and cities more generally.
BibTeX - Entry
@InProceedings{wang_et_al:LIPIcs.GIScience.2023.81,
author = {Wang, Boyu and Crooks, Andrew},
title = {{Agent-Based Modeling of Consumer Choice by Utilizing Crowdsourced Data and Deep Learning}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {81:1--81:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2023/18976},
URN = {urn:nbn:de:0030-drops-189769},
doi = {10.4230/LIPIcs.GIScience.2023.81},
annote = {Keywords: aspect-category sentiment analysis, consumer choice, agent-based modeling, online restaurant reviews}
}
