概括
这项研究引入了一个新的数据集,包括132,421条阿拉伯语推特,分类为垃圾邮件或非垃圾邮件 (ham). 该资源帮助研究人员开发用于阿拉伯自然语言处理 (NLP) 的自动垃圾邮件检测模型.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 数据科学数据科学数据科学
- 网络安全 网络安全
- 社交网络分析 社交网络分析
背景情况:
- 标记阿拉伯语数据集的稀缺性给开发强大的NLP模型带来了挑战.
- 垃圾邮件内容的自动分类对于保持清洁的在线通信至关重要.
研究的目的:
- 为垃圾邮件检测研究引入一个新的,公开可用的阿拉伯语推文数据集.
- 为评估和推进内容分类中阿拉伯NLP技术提供一个基准.
主要方法:
- 采集了132,421条使用Twitter REST API (2021年1月27日 - 2021年3月10日) 的阿拉伯语推文.
- 开发了一个基于Python的爬虫程序,用于数据采集.
- 将推特分为"哈姆" (非垃圾邮件) 和"垃圾邮件"类别.
主要成果:
- 一个包含132,421条阿拉伯语推特的综合数据集,标记为垃圾邮件/非垃圾邮件内容,现在可用了.
- 该数据集是阿拉伯语NLP和相关领域的研究人员的宝贵资源.
结论:
- 这一数据集解决了对阿拉伯语NLP资源的关键需求,特别是在垃圾邮件检测方面.
- 预计这种基准数据集的可用性将促进阿拉伯语言自动化内容分析的进步.
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