One of the most important problems in e-commerce is the accurate calculation of post-purchase ratings given to products. Solving this problem means providing more customer satisfaction for e-commerce sites, highlighting the product for sellers, and ensuring a seamless shopping experience for buyers. Another problem is the correct sorting of product reviews. Misleading reviews standing out can directly affect the sale of the product, causing both financial loss and customer loss. Solving these two main problems will increase sales for e-commerce sites and sellers, while customers will complete their purchase journey seamlessly.
This dataset containing Amazon product data includes various metadata with product categories. The user ratings and reviews of the product with the most reviews in the electronics category are included.
| Variable | Description |
|---|---|
| reviewerID | User ID |
| asin | Product ID |
| reviewerName | User Name |
| helpful | Helpful rating score |
| reviewText | Review text |
| overall | Product rating |
| summary | Review summary |
| unixReviewTime | Review time (Unix timestamp) |
| reviewTime | Review time (Raw) |
| day_diff | Number of days since the review was made |
| helpful_yes | Number of times the review was found helpful |
| total_vote | Total number of votes given to the review |