One of the most important problems in e-commerce is the correct calculation of the points given to the product after sales. The solution to this problem means to provide customer satisfaction for e-commerce site, to make product to stand out for sellers and hassle-free shopping experience for buyers.
Another problem is correct ordering of the comments given to the product. Occurrence of misleading comments can have direct impact on product sales, leading to the loss of both customers and financial resources. As the e-commerce site and sellers tackle these two core challenges, they can boost their sales while ensuring a smooth and hassle-free purchasing journey for customers
In this dataset, users comment and give points to the product. Our goal is to evaluate the reviews according to time-based weighted average and sort them correctly
This dataset contains categories and various metadata of product in Amazon. It has the user ratings and reviews of the electronic category's most reviewed product
| Index | Column | Description |
|---|---|---|
| 1 | reviewerID | User Id |
| 2 | asin | Product Id |
| 3 | reviewerName | User Name |
| 4 | helpful | Useful Evaluation Degree |
| 5 | reviewText | Evaluation |
| 6 | overall | Product Rating |
| 7 | summary | Evaluation Summary |
| 8 | unixReviewTime | Evaluation Time |
| 9 | reviewTime | Evaluation Time {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 |