Dirty Comments, Clean Plates

Dirty comments and clean plates: Using restaurant reviews to predict failed health inspections & predict fake reviews

Our task: use a corpus of text-based reviews to train a model to classify if a restaurant is likely to fail a health inspection and predict if a review is human-generated or generated by GPT 3.5 or 4.

Our project has had the following phases:

• attempting to use Chicago inspections and Yelp review data
• pivoting to a philadelphia-based approach when our scraping efforts were throttled
• creating NN and transformer-based models
• building a fake reviews dataset
• applying our binary-classification models on the fake reviews labeled dataset

This repository has the following directories, which include code for how we constructed our project:

• collect/: contains two sub-directories that chronicle our data collection efforts for both the Chicago-based approach and the philadelpha approach.
  • chicago/datatypes.py: ReviewDataset class for reading in scaped yelp data (Jack, 25 lines)
  • chicago/get_inspections.py: Gets inspections and restaurants jsons, cleans and merges them (Claire, 90 lines)
  • chicago/get_restaurants_by_point.py: Pulls top 1000 restaurants given a list of lat/long coordinates (Claire, 85 lines)
  • chicago/identify_points.py: Given chicago geographic boundaries, identifies n random points (Claire, 59 lines)
  • chicago/yelp_scrape.py: Scrapes yelp reviews given a business id (Jack, 127 lines)
  • philadelphia/merge.py: merges inspection and review datasets for philadelphia (Jack & Claire, 145 lines)
  • philadelphia/merge.py: merges inspection and review datasets for philadelphia (Jack & Claire, 145 lines)
  • yelp/yelp_cleaning.py: subsets all yelp reviews to only those by verified users (Raul, 80 lines)
• data/: houses outputs from both data collection processes (no code here, just intermediate outputs)
• eda/: contains a few notebooks that explore the merged philadelphia dataset
  • eda.py: overview of merged philadelphia data (Benja, 150 lines)
  • graphs_plots.py: plots for eda (Raul, 100 lines)
  • yelp_data_eda.py: counts for yelp data for getting an idea of what is available (Claire, 30 lines)
• models/: contains a few key custom classes/functions that build our modeling pipeline. The rest of the files are notebooks that use these custom packages to run our models and report results.
  • dataloaders.py: custom dataset classes and vectorizers for text processing (Claire & Jack, 215 lines)
  • features.py: data cleaning to make the feature variables into numeric arrays (Jack, 200 lines)
  • shared_models.py: 5 developed models (Claire & Jack, 88 lines)
  • helpers.py: helpers for training models and displaying results, adapted from class functions (Claire, 97 lines)
  • run_models.ipynb: implements all Logistic/SVN models (Claire, 100 lines)
  • all other ipynb notebooks: implements BERT and RNN models (Jack, ~100 lines per script)
• chat_gpt/: contains our pipeline to read in "fake" reviews from ChatGPT 3.5 and 4.0.
  • generate_reviews.py: Uses the openAI API to generate fake reviews (Benja, 200 lines)