在线会议应用程序的自我投票分类模型审查情绪分析和主题建模
Naila Aslam1, Kewen Xia1, Furqan Rustam2
1School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China.
PeerJ. Computer science
|June 22, 2023
概括
本研究介绍了自我投票分类 (SVC),这是一个整体方法,用于增强在线会议应用程序的情绪分析. 这种新的方法显著提高了机器学习模型在分析用户反方面的准确性.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 软件工程 软件工程 软件工程
背景情况:
- 在线会议应用程序对于沟通,教育和商业至关重要,特别是在COVID-19后.
- 用户通过评论反对于改进这些应用程序至关重要.
- 准确的情绪分析对于从反中了解用户意见至关重要.
研究的目的:
- 提出和评估一种新的组合方法,即自我投票分类 (SVC),用于在线会议应用程序评论的情绪分析.
- 通过SVC提高传统机器学习模型的性能.
- 使用隐性迪里克莱特分配 (LDA) 进行主题智能情绪分析.
主要方法:
- 开发了一种自我投票分类 (SVC) 方法,训练多个模型变体,使用不同的特征提取方法 (词包,TF-IDF,哈希).
- 应用SVC与支持向量机 (SVM),决策树,物流回归和K-最近邻近模型.
- 利用硬投票 (HV) 和软投票 (SV) 两个标准来组合模型预测.
- 用于主题建模和主题智能情绪分析的隐性迪里克莱特分配 (LDA).
主要成果:
- 提出的SVC方法大大提高了传统机器学习模型的性能.
- 支持矢量机 (SVM) 通过使用SVC.分别使用硬投票和软投票标准,实现了完美的 (1.00) 和近乎完美的 (0.98) 准确度得分.
- 证明了集体方法在在线会议应用的情绪分析中的有效性.
结论:
- 自选分类 (SVC) 是提高在线会议应用中的情绪分析准确性的有效策略.
- 合并技术,特别是SVC技术,比传统的单一模型方法提供了显著的性能提升.
- 该研究提供了一种强大的方法来分析应用程序评论中的用户情绪和主题.
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