Cross-Domain Consumer Review Analysis

TL;DR

This paper conducts a large-scale cross-domain review analysis across Amazon, Yelp, Steam, and IMDb datasets using Hadoop and Spark to uncover trends in customer sentiment and satisfaction over time.

Contribution

It introduces a scalable framework for analyzing multi-domain reviews and provides insights into customer behavior and sentiment patterns across different online platforms.

Findings

Identifies review distribution trends over time.

Analyzes relationships between review attributes and sentiment.

Provides cross-domain insights into customer satisfaction.

Abstract

The paper presents a cross-domain review analysis on four popular review datasets: Amazon, Yelp, Steam, IMDb. The analysis is performed using Hadoop and Spark, which allows for efficient and scalable processing of large datasets. By examining close to 12 million reviews from these four online forums, we hope to uncover interesting trends in sales and customer sentiment over the years. Our analysis will include a study of the number of reviews and their distribution over time, as well as an examination of the relationship between various review attributes such as upvotes, creation time, rating, and sentiment. By comparing the reviews across different domains, we hope to gain insight into the factors that drive customer satisfaction and engagement in different product categories.