wall-e28 / amazon_vine_analysis

The purpose of the project is to analyze Amazon reviews written by members of the paid Amazon Vine program using AWS, PySpark, and SQL.

Amazon Vine Analysis

Overview

The purpose of the project is to analyze Amazon reviews written by members of the paid Amazon Vine program. In order to complete this analysis, I used a subset of data that pertained to the video game subcategory of Amazon. I then performed an ETL on this data by using AWS, Google Colaboratory, PostgreSQL, and PySpark. After that, I took a closer look at one of the tables I made during the ETL phase regarding the vine reviews to determine if there was any positivity bias for reviews in the Vine Program.

Results

During this analysis I wanted to answer a few key questions.

1. How many Vine reviews and non-Vine reviews were there?
2. How many Vine reviews were 5 stars? How many non-Vine reviews were 5 stars?
3. What percentage of Vine reviews were 5 stars? What percentage of non-Vine reviews were 5 stars?

Here are my answers to these questions.

1. There are 94 vine reviews and 40,471 non-vine reviews for a total of 40,565 reviews.

2. There were 48 5-star vine reviews and 15663 5-star non-vine reviews for a total of 15,711 5-star reviews.

3. 51.06% of paid reviews are 5-stars and 38.7% of unpaid reveiws are 5-stars.

Summary

After looking that the results, I would conclude that there is a positivity bias for reviews in the Vine program. It is important to note that while the non-vine sample size was very large, the vine sample size was less than 100 entries. While 94 entires is still a decent number to sample with, it could lead a less signifcant result. In addition to the current analysis, I could take it a step further and see the percentage of those who purchased the product by filtering through the verified_purchase column to either confirm or fail to confirm if there is a positivity bias for reviews in the Vine program.