@inproceedings{2bf011c9e2124c108e2b3e189f8ea25b,
title = "Agent-Based Modeling of Consumer Choice by Utilizing Crowdsourced Data and Deep Learning",
abstract = "People{\textquoteright}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{\textquoteright} 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{\textquoteright} opinion aspects on places within urban areas. Such data is then used as a basis to inform an agent-based model, where consumers{\textquoteright} (i.e., agents{\textquoteright}) choices are based on their characteristics and preferences. The results show the emergent patterns of reviewers{\textquoteright} 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.",
keywords = "agent-based modeling, aspect-category sentiment analysis, consumer choice, online restaurant reviews",
author = "Boyu Wang and Andrew Crooks",
note = "Publisher Copyright: {\textcopyright} Boyu Wang and Andrew Crooks.; 12th International Conference on Geographic Information Science, GIScience 2023 ; Conference date: 12-09-2023 Through 15-09-2023",
year = "2023",
month = sep,
doi = "10.4230/LIPIcs.GIScience.2023.81",
language = "English",
series = "Leibniz International Proceedings in Informatics, LIPIcs",
publisher = "Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing",
editor = "Roger Beecham and Long, \{Jed A.\} and Dianna Smith and Qunshan Zhao and Sarah Wise",
booktitle = "12th International Conference on Geographic Information Science, GIScience 2023",
}