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Amazon product co-purchasing network and ground-truth communities

Amazon product co-purchasing network and ground-truth communities

Stefan
Open Dataset
Creators: Yang, Jaewon; Leskovec, Jure
Publication Date: 2012
Creators: Yang, Jaewon; Leskovec, Jure

This dataset provides a comprehensive view of product relationships on Amazon, based on the “Customers Who Bought This Item Also Bought” feature. Products are represented as nodes, and an undirected edge between two products signifies frequent co-purchasing, reflecting consumer buying patterns and product associations. ​If a product i is frequently co-purchased with product j, the graph contains an undirected edge from i to j. Each product category provided by Amazon defines each ground-truth community. We regard each connected component in a product category as a separate ground-truth community. We remove the ground-truth communities which have less than 3 nodes. We also provide the top 5,000 communities with highest quality which are described in our paper. The dataset has a size of 0,01 GB and encompasses 334,863 nodes (products) and 925,872 edges (co-purchasing relationships).

The dataset is structured into:​

  • Network Data: An undirected graph where nodes represent products, and edges indicate co-purchasing relationships.​

  • Ground-Truth Communities: Each product category defined by Amazon serves as a ground-truth community. Connected components within these categories are treated as separate communities, excluding those with fewer than three nodes. Additionally, the dataset provides the top 5,000 communities with the highest quality, as detailed in the associated research paper.

Amazon Reviews: Unlocked Mobile Phones

Amazon Reviews: Unlocked Mobile Phones

Stefan
Open Dataset
Creators: PromptCloud, Inc.
Publication Date: 2019
Creators: PromptCloud, Inc.
We analyzed more than 400,000 reviews of close to 4,400 unlocked mobile phones sold on Amazon.com to find out insights with respect to reviews, ratings, price and their relationships, making it a rich resource for analyzing customer sentiment and product performance. The dataset is approximately 0,13 GB in size and is available in CSV format. The author found that on Amazon’s product review platform most of the reviewers have given 4-star and 3-star ratings. The average length of the reviews comes close to 230 characters. They also uncovered that lengthier reviews tend to be more helpful and there is a positive correlation between price & rating. 

Structurally, each entry in the dataset includes the following variables:​

  • Product Name: The name of the product (e.g., “Sprint EPIC 4G Galaxy SPH-D7”).​
  • Brand Name: The manufacturer or parent company (e.g., “Samsung”).​
  • Price: The listed price of the product, with values ranging from a minimum of $1.73 to a maximum of $2,598, and an average price of $226.86.​
  • Rating: The user-assigned rating, ranging between 1 and 5 stars.​
  • Reviews: The textual content of the user’s review, detailing their experience and opinions.​
  • Review Votes: The number of helpfulness votes each review received from other users, with a minimum of 0, a maximum of 645, and an average of 1.50 votes.​

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