One of the most critical challenges in e-commerce is accurately calculating product ratings based on post-purchase feedback.
Solving this problem leads to:
- Improved customer satisfaction
- Better product visibility for sellers
- A more seamless shopping experience for buyers
Another key challenge is properly sorting product reviews. Misleading or low-quality reviews appearing at the top can directly impact product sales, leading to:
- Financial loss
- Loss of customer trust
By addressing these two core problems:
- E-commerce platforms and sellers can increase sales
- Customers can complete their purchasing journey smoothly and confidently
This dataset contains Amazon product data, including product categories and various metadata.
It focuses on:
- The most-reviewed product in the electronics category
- User ratings and review data
| Variable | Description |
|---|---|
| reviewerID | User ID |
| asin | Product ID |
| reviewerName | User Name |
| helpful | Helpfulness rating |
| reviewText | Review text |
| overall | Product rating |
| summary | Review summary |
| unixReviewTime | Review timestamp |
| reviewTime | Raw review time |
| day_diff | Number of days since the review |
| helpful_yes | Number of helpful votes |
| total_vote | Total number of votes |
- Calculate time-weighted average rating
- Compare it with the standard average rating
- Identify the top 20 most helpful reviews
- Apply scoring methods:
score_pos_neg_diffscore_average_ratingwilson_lower_bound
- Time-based weighting of ratings
- Review scoring using statistical methods
- Ranking reviews based on Wilson Lower Bound
- More reliable product ratings
- Better review ranking system
- Improved user trust and engagement