一个主观性分类的数据集在印度尼西亚的叫车应用程序审查
Violeta Arifin1, Yuriashi Adelia Putri1, Richard Wiputra1
1Information Systems Department, School of Information Systems, Bina Nusantara University, Indonesia.
Data in brief
|January 6, 2026
概括
本研究引入了印尼乘车评价的新数据集,以分类主观和客观反. 此资源有助于开发更好的自然语言理解低资源语言.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 情绪分析 情绪分析
背景情况:
- 了解用户反对于服务评估至关重要,尤其是在印度尼西亚日益增长的乘车呼叫市场.
- 区分主观和客观评论是准确解释用户感知的关键.
- 低资源语言对自然语言理解 (NLU) 模型开发提出了独特的挑战.
研究的目的:
- 为主观性分类提供高质量的,手动注释的印尼乘车应用程序评论数据集.
- 促进对低资源语言的主观性检测和NLU的研究.
- 为评估和开发高级语言模型提供一个基准.
主要方法:
- 从谷歌Play商店收集了1338个印度尼西亚叫车的评论.
- 预处理审查以删除不相关的元素,如URL和表情符号.
- 由两个独立的注释者进行手动注释,然后以共识为基础对高质量的标签进行裁决.
主要成果:
- 开发了1338个印尼乘车评价的数据集,具有可靠的主观性分类.
- 数据集结构为可重复分析,并支持对主观性检测模型的可靠评估.
- 在印尼语和其他东南亚语言中为NLU研究建立了宝贵的资源.
结论:
- 本文所介绍的数据集是对低资源语言主观性检测研究的重要贡献.
- 该资源可用于对监督分类器进行比较,并对多语言和大型语言模型进行评估.
- 它为开发智能系统在数字服务环境中解释用户反提供了基础.
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