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. Since misleading comments will directly affect the sale of the product, it will cause both financial loss and loss of customers. In the solution of these 2 basic problems, e-commerce site and sellers will increase their sales, while customers will complete their purchasing journey without any problems.
This dataset consist of Amazon product data, and it includes product categories and various metadata. The product with the most reviews in the electronics category has user ratings and reviews.
There are 12 different variables, around 4915 observations in the dataset.
reviewerID : ID of the reviewer, e.g. A2SUAM1J3GNN3B
asin : ID of the product, e.g. 0000013714
reviewerName : name of the reviewer
helpful : helpfulness rating of the review, e.g. 2/3
reviewText : text of the review
overall : rating of the product
summary : summary of the review
unixReviewTime : time of the review (unix time)
reviewTime : time of the review (raw)
day_diff : number of days since review was written (in days)
helpful_yes : the number of times the review was found useful
total_vote : number of votes given to the review
- Clone this repository
https://github.com/nedimcanulusoy/Amazon-Rating-Product-Sorting-Reviews.git
- Change directory to the cloned repository
cd Amazon-Rating-Product-Sorting-Reviews
- Open the notebook and run the cells
The data set is not included due to General Data Protection Regulation (GDPR) rules.
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