Customer-Feedback-Insight-System-

This system will provide small businesses with easy-to-understand reports on the platform. Specifically:

1. Identify business attributes, cities, and states that lead to positive/negative reviews.
2. Uncover emerging trends and customer sentiments.

Initial Datasets Pulling: https://www.yelp.com/dataset

Datasets used: yelp_academic_dataset_business.json, yelp_academic_dataset_review.json

Tools

1. AWS S3 Bucket
2. AWS Sagemaker Notebook
3. AWS RDS Postgre SQL
4. AWS VPC public & private subnets
5. AWS EC2 instance SSH & Flask

Cloud Infrastructure Design

Final Files:

1. sample_df.csv
2. flaskDB.py

Final run execution

1. run flaskDB.py in ec2 instance

Note:

1. ‘Cleaned_sampled_dataset.csv’ is taken 1GB sample of the overall dataset(8GB+)
2. ‘sample_df.csv’ is the result from ‘Cleaned_sampled_dataset.csv’ after executing this file ’Sentiment Analysis’.
3. ‘sample_df.csv’ has been transformed into RDS Postgre database by running ‘RDSPostgre.py’.
4. run flaskDB.py in ec2 instance
5. Interacting with the website: 44.197.239.78:5001