This project analyze Amazon reviews written by members of the paid Amazon Vine program. The Amazon Vine program is a service that allows manufacturers and publishers to receive reviews for their products. Companies like SellBy pay a small fee to Amazon and provide products to Amazon Vine members, who are then required to publish a review.

The Amazon Vine program has about 50 datasets. Each one contains reviews of a specific product, from clothing apparel to wireless products. In this project, we pick one of these datasets and use PySpark to perform the ETL process to extract the dataset, transform the data, connect to an AWS RDS (PostgreSQL) instance, and load the transformed data into pgAdmin. Next, we use PySpark to determine if there is any bias toward favorable reviews from Vine members in the dataset. Then, we write a summary of the analysis.

This analysis use the Pet Products Reviews from the Amazon Review datasets as the data source.

1. How many Vine reviews and non-Vine reviews were there?
   • There are 170 Vine reviews
   • There are 37,840 non-Vine reviews
2. How many Vine reviews were 5 stars? How many non-Vine reviews were 5 stars?
   • There are 65 5-stars Vine reviews
   • There are 20,612 5-stars non-Vine reviews
3. What percentage of Vine reviews were 5 stars? What percentage of non-Vine reviews were 5 stars?
   • 38.24% of Vine reviews were 5-stars
   • 54.47% of non-Vine reviews were 5-stars

• Since the percentage of 5-stars reviews of the Vine program (38.24%) is much lower than the non-Vine reviews (54.47%), this indicates that having a paid Vine review may have bias on the review. However, we cannot conclude the reasons of causing the difference.
• We may run the same analysis on other Amazon Review datasets, and compare the percentage of Vine & non-Vine 5-stars reviews, and see if there are similar results or not.