Purpose: The purpose of this project was to create flexible code that can quickly perform ETL on Amazon reviews datasets and analyze how helpful Amazon's paid "Vine" reviews are for these products. To accomplish this I performed the follwing:

1. Used Amazon Webservices to create a relational database instance (PostgreSQL)
2. Used pgAdmin to create tables to store the Amazon Review Data
3. Used GoogleColab to create a PySpark Session and Performed ETL on the datasets
4. Used GoogleColab and PySpark to determine if Vine reviews (paid) are helpful to consumers relative to unpaid reviews

Amazon Review Datasets

https://s3.amazonaws.com/amazon-reviews-pds/tsv/index.txt

PySpark ETL Code

ETL Code

Vine Analysis Code

Musical Instruments

Digital Video Games

Analysis Overview: The key objective of the analysis was to compare paid vs unpaid reviews. I originally chose to analyze datasets from Digital Videogames, however, this dataset did not contain any paid reviews. So, instead I analyzed reviews for Muiscal Instruments.

In order to compare paid and unpaid reviews I only analyzed reviews that had a high number of votes (<=20). I then filtered the dataframes so that the ratio of helpful votes to total votes was at least 50%. I then filtered the dataframe based on reviews from the paid service (Vine == "Y") or unpaid (Vine == "N") and compared the total number of reviews, the number of 5-star reviews, and the percentage of 5-star reviews in each category.

Results for Paid Reviews for Musical Instruments

Total Number of Reviews = 59

Number of 5-Star Reviews = 34

Percentage of 5-Star Reviews = 57.6%

Results for Unpaid Reviews for Musical Instruments\

Total Number of Reviews = 13,480

Number of 5-Star Reviews = 7,678

Percentage of 5-Star Reviews = 57.0%

The code provided here is an efficient way of performing ETL using Amazon Webservices, PostgreSQL/pgAdmin, GoogleColab, and PySpark. In addition, the code provides basic analyses on how helpful paid and unpaid reviews are for different products. In my analysis, I looked more closely at how paid vs unpaid reviews differed for musical instruments. The results indicate that both paid and unpaid reviews achieved 5-star reviews at similar percentage of times. Therefore, paying for reviews does not appear to bias reviews towards more 5-star ratings for musical instruments. Based on this preliminary analysis, it is unclear if paying for reviews for musical instruments is actually worth the cost. It is possible that different a different product group might be more susceptible to positive review bias. Fortunately, the code provided here can easily be used to analyze any of the 50 other product datasets.