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yelp-abm

https://github.com/wang-boyu/yelp-abm

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 3 DOI reference(s) in README
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    Academic publication links
    Links to: zenodo.org
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    Academic email domains
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    Scientific vocabulary similarity
    Low similarity (6.6%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: wang-boyu
  • Language: Python
  • Default Branch: main
  • Size: 4.35 MB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 3 years ago · Last pushed over 2 years ago
Metadata Files
Readme Citation

README.md

Yelp Opinion Dynamics Model

DOI

Summary

This is an agent-based model to simulate consumer agents and their restaurant visiting patterns.

During model initialization, restaurants are created with locations using information from Yelp, along with their average sentiment scores from a pre-trained language model. Consumer agents are created at random places with a random attribute (i.e., student, middle-aged, or senior), which subsequently determines their restaurant preferences. For example, student agents are more sensitive to the price factor, whereas senior agents prefer restaurants with higher ambience score.

At each step, consumer agents are informed by the language model results and visit the best restaurant based on their preferences. We also implement a null model in which consumer agents make random decisions on which restaurant to visit.

GeoSpace

The City of St. Louis, MO.

GeoAgent

Restaurant agents: each restaurant has a location and sentiment scores on four choice factors: food, service, price, and ambience, estimated from a pre-traind language model on text reviews from Yelp.

Consumer agents: each consumer has a random location and a preference on the four choice factors. The preference is determined by the consumer's attribute (i.e., student, middle-aged, or senior).

How to run

To install the dependencies:

bash python3 -m pip install -r requirements.txt

To run the model interactively, run mesa runserver in this directory. e.g.

bash mesa runserver

Then open your browser to http://127.0.0.1:8521/ and press Start.

How to cite

To cite this model in your publication, you can use the CITATION.bib.

Owner

  • Name: Wang Boyu
  • Login: wang-boyu
  • Kind: user
  • Location: Buffalo, NY
  • Company: University at Buffalo

PhD Student in Geography at the University at Buffalo. Research in Agent-Based Modelling, GIScience, and Machine Learning.

Citation (CITATION.bib) 

@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}
}

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Dependencies

requirements.txt pypi
  • black *
  • mesa-geo ==0.5.0
  • pyarrow *
  • python-dotenv *
setup.py pypi