One of the most important problems in e-commerce is the correct calculation of the points given to the products after sales. The solution to this problem means providing greater customer satisfaction for the e-commerce site, prominence of the product for the sellers and a seamless shopping experience for the buyers. Another problem is the correct ordering of the comments given to the products. The prominence of misleading comments will cause both financial loss and loss of customers. In the solution of these 2 basic problems, while the e-commerce site and the sellers will increase their sales, the customers will complete the purchasing journey without any problems.
This dataset containing Amazon Product Data includes product categories and various metadata. The product with the most comments in the electronics category has user ratings and comments.
| Sr. | Feature | Description |
|---|---|---|
| 1 | reviewerID | ID of the reviewer, e.g. A2SUAM1J3GNN3B |
| 2 | asin | ID of the product, e.g. 0000013714 |
| 3 | reviewerName | Name of the reviewer |
| 4 | helpful | Helpfulness rating of the review |
| 5 | reviewText | Text of the review |
| 6 | overall | Rating of the product |
| 7 | summary | Summary of the review |
| 8 | unixReviewTime | Time of the review (unix time) |
| 9 | reviewTime | Time of the review (raw) |
| 10 | day_diff | Number of days since assessment |
| 11 | helpful_yes | The number of times the evaluation was found useful |
| 12 | total_vote | Number of votes given to the evaluation |
The dataset is not shared because it's exclusive to Miuul Data Science & Machine Learning Bootcamp.