地球资源数据云——数据资源详情
亚马逊评论的情感分析
该数据集《amazon reviews for sentiment analysis》主要用于多分类任务，数据形态以文本为主，应用场景偏向文本内容分析。 题目说明：amazon NLP - Sentiment Analysis 任务类型：文本多分类。 建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：amazon_reviews.csv。 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 This dataset containing Amazon Product Data includes product categories and various metadata. 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.
摘要概览
该数据集《amazon reviews for sentiment analysis》主要用于多分类任务，数据形态以文本为主，应用场景偏向文本内容分析。 题目说明：amazon NLP - Sentiment Analysis
任务类型：文本多分类。
建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：amazon_reviews.csv。
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 This dataset containing Amazon Product Data includes product categories and various metadata.
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.