BI-SENT:基于双语方面的情绪分析,对乌尔都语的COVID-19推特进行了双语分析
Ehtesham Hashmi1, Amna Altaf2, Muhammad Waqas Anwar3
1Department of Information Security and Communication Technology, Norwegian University of Science and Technology, Innlandet, Norway.
PloS one
|June 13, 2025
概括
这项研究为乌尔都语和罗马乌尔都语引入了无监督的基于方面的情感分析,改善了不需要手动注释的情感理解. 它提供了关于COVID-19大流行期间公众情绪的宝贵见解.
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
- 社交媒体分析 社交媒体分析
背景情况:
- COVID-19大流行对全球心理健康产生了重大影响,使人们越来越依赖社交媒体来表达情感.
- 通过情绪分析了解公众情绪至关重要,特别是在乌尔都语等资源较低的语言中.
- 乌尔都语的现有方面级情绪分析受到昂贵的手动数据注释的限制.
研究的目的:
- 为乌尔都语和罗马乌尔都语开发一种创新的,无监督的,双语的基于方面情感分析 (ABSA) 系统.
- 为了克服乌尔都语语言在ABSA中手动注释的挑战.
- 在COVID-19大流行期间,为公众情绪提供更深入的见解.
主要方法:
- 对于乌尔都语: 实施了基于规则的语法方法来提取方面术语.
- 对于罗马乌尔都语:利用调配模式和主题建模来识别和分类方面.
- 采用无监督的方法来消除手动注释数据集的需要.
主要成果:
- 乌尔都语方面术语提取实现了83%的准确性,比以前的方法提高了5%的F1分数.
- 罗马乌尔都语主题建模的困惑度得分为-7和连贯度得分为41,表明语义连贯性.
- 证明了无监督技术在乌尔都语和罗马乌尔都语方面层面情绪分析中的有效性.
结论:
- 开发的无监督双语ABSA系统显著推进了乌尔都语语言的情绪分析.
- 消除手动注释使得面向层面的情绪分析更加可行和可扩展.
- 这些发现提供了对与流行病相关的公众情绪的关键见解,有助于政策制定者和卫生组织.
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