This is a Sentiment Analyzer repo of Restaurant Reviews from websites like Zomato, Foursquare, Yelp & Burrp. It finds Top-k dishes of every restaurant in a city with its popularity index that is useful for recommending top dishes to customers depending on their cuisine and taste preferences.
The objective of this project is to build a Sentiment Analyzer of Restaurant Reviews from websites like Zomato, Foursquare, Yelp & Burrp and to find Top-k dishes of every restaurant in a city with its popularity index that is useful for recommending top dishes to customers depending on their cuisine and taste preferences.
Python scripts for scraping reviews from Zomato, foursquare and yelp are written. Reviews are cleaned, pre-processed and explored for patterns. Machine learning model is built with the best features for discriminating positive and negative sentiments in the reviews. The built model is optimized for generalization and accuracy. The optimized model is then validated and evaluated.
Input : Reviews from Zomato, Foursquare, Yelp and Burrp
Output : Top-k dishes of every restaurant in a city with its popularity index
- Python
- Scikit-learn
- Pandas
- Matplotlib
Find the report here
Please feel free to connect with me to run this code locally at vejeysubash@gmail.com