在数据中毒攻击下创建一个没有偏见的数据集,其中包括食品配送应用程序的评论
Hyunmin Lee1, SeungYoung Oh1, JinHyun Han1
1Department of Computer Science and Engineering, University of Seoul, Seoul 02504 Republic of Korea.
Data in brief
|July 8, 2024
概括
这项研究引入了韩国食品配送应用程序评论的新数据集,解决了缺乏关于"评论活动"的数据的问题,即免费物品被交换成评论. 该数据集有助于开发更公平的AI来分析在线评论.
科学领域:
- 数据科学数据科学数据科学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 在线食品配送应用程序依赖于客户评论,但"评论活动" (用评论交换免费服务) 的实践会扭曲数据.
- 现有的数据集缺乏韩国审查,并且不考虑审查事件等审查操纵策略.
- 这种差距阻碍了对情绪分析和审查真实性的公正人工智能模型的开发.
研究的目的:
- 展示韩国食品配送应用程序审查的新型数据集,特别是捕捉审查事件策略的实例.
- 为研究评论操纵及其对在线评论生态系统的影响提供资源.
- 支持创建更公平的人工智能培训数据,用于食品配送部门的情绪分析.
主要方法:
- 使用Python的Selenium库搜索网页,从韩国食品配送应用程序收集评论.
- 数据提取的重点是使用审查活动策略的餐厅的评论.
- 数据集编制,包括评论文本,评分 (总体,口味,数量,交货),订单细节,时间和图像的包含.
主要成果:
- 从136家韩国餐厅创建了一个包含128,668条评论的综合数据集.
- 该数据集包括丰富的元数据,对于分析评论真实性和情绪至关重要.
- 这个资源可以研究数据中毒攻击,并开发无偏见的AI模型.
结论:
- 提出的数据集填补了食品交付应用程序审查分析现有资源的关键缺口.
- 它为研究人员调查审查操纵策略和情绪分析提供了宝贵的工具.
- 该数据集有助于在构建更公平,更可靠的AI系统中取得进展,用于在线审查解释.
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