一种基于依赖的混合方法,用于乌尔都语情绪分析.
Urooba Sehar1, Summrina Kanwal2,3, Nasser I Allheeib4
1Capital University of Science & Technology, Islamabad, 44000, Pakistan.
Scientific reports
|December 12, 2023
概括
这项研究为乌尔都语社交媒体数据引入了一种新的概念级情绪分析框架. 该方法结合了语言规则和深度神经网络,比现有方法提高了准确性.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 社交媒体每天都会产生大量的非结构化数据.
- 现有的情绪分析 (SA) 方法通常依赖于低效的单词共发生频率.
- 需要更准确的SA,特别是像乌尔都语这样的低资源语言.
研究的目的:
- 在乌尔都语中介绍一个概念级情绪分析 (SA) 的新框架.
- 通过超越单词级别分析来提高SA的准确性.
- 利用基于规则的和深度学习的方法来改进情绪检测.
主要方法:
- 从YouTube评论和演讲中创建了一个全面的乌尔都语数据集.
- 该框架将预定义的语言规则与深度神经网络 (DNN) 集成在一起.
- 它采用一种混合方法:基于规则的情感流从单词流向概念,用于句子分类的DNN备用.
主要成果:
- 提出的概念级SA框架比最先进的方法实现了6-7%的改进.
- 它在乌尔都语数据集上表现优于LSTM,CNN,SVM,LR和MLP等传统方法.
- 基于规则的混合模型和DNN模型展示了极性检测的卓越准确性.
结论:
- 这项研究为乌尔都语引入了一个有效的概念级情绪分析框架.
- 将语言规则和DNN结合在一起,在乌尔都语SA.提供了显著的进步.
- 该框架显示了分析不同乌尔都语社交媒体内容的情绪的前景.
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