整合多标准决策与混合深度学习,用于推系统中的情绪分析
Swathi Angamuthu1, Pavel Trojovský1
1Department of Mathematics, University of Hradec Králové, Rokitanskeho, Hradec Kralove, Czech Republic.
PeerJ. Computer science
|September 14, 2023
概括
我们为推系统引入了一种新的方法,该方法使用情绪分析来解释专家评论. 这种方法通过分析自然语言来增强决策,达到98%的情绪分析准确度.
科学领域:
- 人工智能的人工智能
- 信息检索 信息检索
- 决策科学 决策科学 决策科学
背景情况:
- 决策模型中的专家评估通常受到预定义的数字或语言术语的限制.
- 在线信息过载需要像推系统这样的智能决策支持技术.
- 传统的推系统依赖于单级算法,限制了细微的用户偏好捕获.
研究的目的:
- 开发一种将自然语言表达的专家判断纳入决策模型的方法.
- 通过分析专家评价和星级等级来增强推者系统,用于多标准决策.
- 通过情绪分析,通过提供更明智的用户选择来解决信息过载.
主要方法:
- 提出了多人多标准决策 (SAR-MCMD) 方法的推系统中的情感分析.
- 利用情绪分析来处理自然语言专家评论和星级评分.
- 集成深度学习与多标准决策,以提高推能力.
- 使用TripAdvisor,TMDB 5000电影和亚马逊的数据集验证了该技术.
主要成果:
- 在专家评价中达到98%的情感分析准确度.
- 与现有方法相比,在准确性,精度,回忆和F1得分方面表现出卓越的表现.
- 该SAR-MCMD系统为用户提供高度准确的建议.
结论:
- 对自然语言专家评价的情感分析可以显著改善决策模型.
- 将深度学习与多标准决策相结合,为高级推系统提供了一种强大的方法.
- SAR-MCMD方法有效地处理复杂的专家判断,以获得更好的建议.
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