AG's News Topic Classification Dataset Version 3, Updated 09/09/2015 ORIGIN AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml, data compression, data streaming, and any other non-commercial activity. For more information, please refer to the link http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html . The AG's news topic classification dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the dataset above. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). DESCRIPTION The AG's news topic classification dataset is constructed by choosing 4 largest classes from the original corpus. Each class contains 30,000 training samples and 1,900 testing samples. The total number of training samples is 120,000 and testing 7,600. The file classes.txt contains a list of classes corresponding to each label. The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 3 columns in them, corresponding to class index (1 to 4), title and description. The title and description are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".

复旦大学中文语料分类数据 3个子文档 一共有19666个文档 是很好的分类语料库

本语料库由复旦大学李荣陆提供。 train.zip共9804篇文档，test.zip共9832篇文档，都分为20个类别。 下载后可以自己重新切分数据，也可以直接用。 免费下载地址：链接:https://pan.baidu.com/s/1E2vUjyBtrlG0SBCkO-_IAQ 密码:dq9m 使用时尽量注明来源（复旦大学计算机信息与技术系国际数据库中心自然语言处理小组）。

新闻栏目中文文本分类，新闻栏目一共有： 体育 5000 时政 5000 房产 5000 家居 5000 财经 5000 时尚 5000 科技 5000 教育 5000 娱乐 5000 游戏 5000 每个新闻栏目拥有5000条新闻，通过对新闻内容作为样本训练模型，使得该模型能够预测出该条新闻所属的栏目。

资源为新闻类的中文文本分类数据集，能够满足机器学习，文字分析方面的需求

50000条新闻文本数据集，文本有9类。可用于文本分类模型训练。