通过人口游戏模型方法推进情感分类
1Department of Applied Mathematics, Delhi Technological University, New Delhi, India.
Scientific reports
|September 4, 2024
概括
本研究引入了使用游戏理论的无监督计算情绪分析方法,消除了对广泛训练数据的需求. 这种新的方法在跨语言和领域的情绪分类方面取得了很高的准确性.
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
- 人工智能的人工智能
背景情况:
- 手动分析大量数字文本的情绪分析具有挑战性.
- 现有的计算情绪分析通常需要广泛的机器学习和预训练.
- 为了在数字内容中高效地理解情感,需要自动化工具.
研究的目的:
- 提出一种创新的无监督方法来对情绪进行分类.
- 克服现有的监督机器学习技术的局限性.
- 开发一种语言独立的情感分析框架.
主要方法:
- 利用游戏理论概念,特别是人口游戏模型.
- 提取的文本特征:从评论评论中获得的上下文得分和情感得分.
- 在认知数学框架内使用词典数据库和数值得分.
主要成果:
- 在不同领域 (酒店,餐厅,电子产品) 的情绪分类中取得了高准确性.
- 在英语 (高达89%的准确度) 和印度语 (高达84%的准确度) 两种语言中都表现出有效性.
- 通过统计分析验证了域名和语言独立性.
结论:
- 提出的无监督,基于游戏理论的模型为传统方法提供了有效的替代方案.
- 该框架与语言无关,并显示出合理性和连贯性.
- 这种方法显著提升了自动化情绪分析能力.
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