The project repository for this article.
Just clone the repository and download the models by using scripts/download_models.py
I haven't included the torch installation in the requirements.txt. That's because the installation procedure wouldn't be same for everyone.
I have used PROJECT_ID and KILI_API_KEY environment variables to pass the API key and the project ID. I was using dotenv to load the environment variables from .env file.
collect_reviews.py
Script to collect the reviews by using google-play-scraper
Arguments
lang (l): The language reviews written in, default: English
country (c): The country to pull the reviews from, default: US
name (n): Package name of the application (Google Play Store)
count (co): Number of reviews to collect, default:10,
allto get all reviewsfilter (f): Integer to filter the reviews by rating, None for all rating. default: None
help: Shows the argument descriptions
Usage
python scripts/collect_reviews.py -co 100 --name medium.com.reader
download_models.py
Downloads the pre-trained models (finetuned models for 40 and 20 runs) into the current working directory. Uses gdown to download the models from google drive. Folder ID can be found in the script file
Arguments
None
Usage
python scripts/download_models.py
move_diff_to_review.py
Moves the distinct elements of the given two datasets into To Review section in the project.
Arguments
first: first CSV dataset to load,
second: second CSV dataset to load
Usage
python scripts/move_diff_to_review.py --first data/processed/dataset-2.csv --second data/processed/dataset-3.csv
prepare_dataset.py
Pulls the labeled and reviewed data separately and saves it with the given file name in data/processed folder.
Arguments
output_name: File name for the pulled dataset. (it should include .csv extension too)
remove (optional): Label to remove from the dataset
Usage
python scripts/prepare_dataset.py --output_name final_dataset.csv