Agent-Based Modeling of Consumer Choice

At the upcoming International Conference on Geographic Information Science (GIScience 2023) Boyu Wang and myself have a new paper entitled "Agent-Based Modeling of Consumer Choice by Utilizing Crowdsourced Data and Deep Learning." In the paper we explore how through mining Yelp reviews can inform an agents choices of restaurants. The model itself was created in Mesa and uses Mesa-Geo and  more details about the model can be found at https://github.com/wang-boyu/yelp-abm.  If this sounds of interest, below you can see the abstract to the paper, some fugues including the graphical user interface of the model and a link to the 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.

Keywords: aspect-category sentiment analysis, consumer choice, agent-based modeling, online restaurant reviews.

An overview of proposed agent-based model logic.

Average star rating vs. average sentiment by aspect category for 200 randomly selected restaurants in the City of St. Louis, MO.

The prototype agent-based model (a) with simulated (b) and actual visiting patterns (c).

Full reference:

Wang, B. and Crooks, A.T. (2023), Agent-Based Modeling of Consumer Choice by Utilizing Crowdsourced Data and Deep Learning, in Beecham, R., Long, J.A., Smith, D., Zhao, Q., and Wise, S (eds), Proceedings of the 12th International Conference on Geographic Information Science (GIScience 2023), Dagstuhl Publishing, Dagstuhl, Germany., pp. 81:1-81:6. (pdf)