amazon reviews for sentiment analysis

One of the most important problems in e-commerce is the correct calculation of the points given to after-sales products. The solution to this problem is to provide greater customer satisfaction for the e-commerce site, product prominence for sellers, and a seamless shopping experience for buyers. Another problem is the correct ordering of the comments given to the products. The prominence of misleading comments will cause both financial losses and customer losses. In solving 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 consists of ranking product ratings and reviews on Amazon. Please review this notebook to observe how I came up with this [dataset](https://www.kaggle.com/code/tarkkaanko/rating-product-sorting-reviews-in-amazon) This dataset containing Amazon Product Data includes product categories and various metadata.

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### What is expected of you?

The product with the most comments in the electronics category has user ratings and comments. In this way, we expect you to perform sentiment analysis with your specific methods.

电子商务领域最为关键的课题之一，便是售后产品积分计算的精确性。解决此问题的核心在于提升电商平台的客户满意度、凸显卖家产品特色，以及为消费者提供无缝的购物体验。另一问题则涉及对产品评论的正确排序。误导性评论的突出展示将导致财务损失及客户流失。在解决这两个基本问题的基础上，电商平台与卖家将提升销售额，而消费者亦能在无任何障碍的情况下完成购物之旅。本数据集包含亚马逊产品评分及评论的排名信息。请查阅此笔记（[dataset](https://www.kaggle.com/code/tarkkaanko/rating-product-sorting-reviews-in-amazon)），以了解我是如何构建该数据集的。数据集内含亚马逊产品数据，包括产品类别及各类元数据。### 您的任务期待您对电子类产品中评论数量最多的产品进行情感分析，运用您特定的方法。