多数投票框架可用于对产品评论的可靠情绪分析
1Business Information Systems, Babes-Bolyai University of Cluj-Napoca, Cluj-Napoca, Romania.
PeerJ. Computer science
|March 10, 2025
概括
本研究引入了一种新的多数投票方法,以提高产品评价中的情绪分析一致性. 这种方法提高了数据可靠性,用于训练更准确的深度学习模型.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 对在线产品评论的情绪分析对企业至关重要.
- 现有的自动化情绪分类方法经常存在不一致性.
- 可靠的情绪数据对于训练有效的机器学习模型至关重要.
研究的目的:
- 开发一个量身定制的多数投票方法,以提高情绪分析的一致性.
- 提高情绪分类结果的可信性和可靠性.
- 创建一个可靠的基础,培养更精确的情绪分析模型.
主要方法:
- 从多个自动化工具中利用情绪标签.
- 实施强有力的多数决策规则,以基于共识的分类.
- 利用共识标记的数据来训练深度学习模型.
主要成果:
- 在使用较少数据的深度学习模型中实现了竞争性准确性.
- 证明了与商业情绪分析工具相比的有效性.
- 确保数据的一致性,以改善模型培训.
结论:
- 量身定制的多数投票方法显著提高了情绪分析的可靠性.
- 这种方法为训练高性能情绪分析模型提供了一致的数据基础.
- 该方法提供了与商业工具相比的成本效益高的解决方案.
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