游戏理论和基于MCDM的餐厅评论的无监督情绪分析
1Department of Applied Mathematics, Delhi Technological University, New Delhi, India.
概括
这项研究引入了一种新的无监督模型,用于情感分析和评论情感分析. 这种新的框架使用数学优化来准确地分类客户的情绪和满意度,而不需要大型预先训练的数据集.
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
- 机器学习 机器学习
背景情况:
- 组织越来越依赖于分析在线客户反以获得商业智能.
- 现有的情绪分析方法往往需要广泛的预培训,导致高计算成本.
- 情绪分析对于了解餐厅等服务行业的细微客户满意度至关重要.
研究的目的:
- 提出一种新的未经监督的情绪分类模型来分析评论数据.
- 开发一个数学优化框架,同时进行情感和情感分析.
- 准确地确定评论情绪极性和客户满意度水平.
主要方法:
- 提出了一个无监督的数学优化框架.
- 该模型在第一阶段整合了审查上下文,评分和情感得分.
- 在第二阶段用于分类,采用非合作性游戏理论方法 (纳什平衡).
主要成果:
- 该模型在两个餐厅评论数据集上实现了最先进的性能.
- 通过统计分析验证了实验结果,证实了显著性.
- 拟议的无监督方法证明了域名和语言独立性.
结论:
- 这种新的无监督框架为传统的机器学习模型提供了一个有效的替代方案,用于情感和情感分析.
- 该模型提供了合理和一致的结果,提高了客户反分析的可靠性.
- 这种方法有效地分类情绪并预测客户满意度,帮助企业提高服务质量.
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