BaitBuster-Bangla:一个全面的数据集用于Bangla的点击诱检测,具有多功能和多模式分析
Abdullah Al Imran1, Md Sakib Hossain Shovon1, M F Mridha1
1Department of Computer Science, American International University-Bangladesh, Dhaka, Bangladesh.
Data in brief
|March 6, 2024
概括
研究人员创建了一个庞大的孟加拉语YouTube点击诱数据集,包含253,070个视频. 这一多模数据集有助于自然语言处理和数据科学在低资源语言.
科学领域:
- 自然语言处理自然语言处理.
- 数据科学数据科学数据科学
- 计算语言学 计算语言学
背景情况:
- 点击诱检测对于内容分析至关重要.
- 像孟加拉语这样的低资源语言缺乏全面的数据集.
- 多模式分析提供了对点击诱的更深入的见解.
研究的目的:
- 为了引入最大的孟加拉语YouTube点击诱数据集.
- 为了促进对低资源语言的点击诱现象的研究.
- 为了实现点击诱特征的多模式分析.
主要方法:
- 通过YouTube API和Python网络自动化自动收集数据.
- 数据集包括253,070个数据点,具有18个不同的特征.
- 进行严格的预处理,以消除,删除和删除偏差.
主要成果:
- 迄今为止最大和最强大的孟加拉语点击诱库.
- 数据集包括元数据,内容,参与和标签.
- 能够在语言和交互层面上进行全面的分析.
结论:
- 该数据集是NLP和数据科学研究人员的宝贵资源.
- 促进了先进的跨语言点击诱检测模型的开发.
- 促进进一步研究孟加拉国多式联运点击诱现象.
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