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Agent-Based Modeling of Consumer Choice by Utilizing Crowdsourced Data and Deep Learning (Short Paper)

Authors Boyu Wang , Andrew Crooks



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Boyu Wang
  • Department of Geography, University at Buffalo, NY, USA
Andrew Crooks
  • Department of Geography, University at Buffalo, NY, USA

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Boyu Wang and Andrew Crooks. Agent-Based Modeling of Consumer Choice by Utilizing Crowdsourced Data and Deep Learning (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 81:1-81:6, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.81

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.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Natural language processing
  • Computing methodologies → Agent / discrete models
  • Social and professional topics → Geographic characteristics
Keywords
  • aspect-category sentiment analysis
  • consumer choice
  • agent-based modeling
  • online restaurant reviews

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References

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