基于特征投影和物联网中的多源关注的跨领域情绪分析
Yeqiu Kong1, Zhongwei Xu1, Meng Mei1
1School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究引入了一种新的跨领域情绪分析方法,以改善物联网 (IoT) 系统的数据标签. 功能投影和多源关注 (FPMA) 方法有效地转移知识并减轻负面转移问题.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 社交媒体是智能系统的宝贵数据来源,但需要情绪分析.
- 跨域情绪分析通过利用标记的源域来解决目标域的数据稀缺问题.
- 现有的方法往往忽略了来自无关的源域的负转移问题.
研究的目的:
- 提出一种新的跨领域情绪分析方法,特征投影和多源注意力 (FPMA).
- 为了减轻负面转移效应并增强特征表示,以改善情绪分类.
- 通过有效的情绪分析,使物联网 (IoT) 传感器能够提供用户喜欢的数据.
主要方法:
- 利用两个特征提取器和域区分器的对抗性训练来捕获共享和私有特征.
- 采用直角投影来优化为多源域分类提取的特征.
- 实施了多源注意力机制,用于在目标域中加权的情绪预测.
主要成果:
- 拟议的FPMA方法在常见数据集上的基线模型相比,显示出更高的性能.
- 通过采用多来源选择策略,FPMA有效地缓解了负面转移.
- 改进的特征表示导致了情绪分类准确度的提高.
结论:
- 对于跨领域的情绪分析,FPMA提供了一个强大的解决方案,特别是在数据稀缺的情况下.
- 该方法成功地解决了域调整中负转移的挑战.
- 对于智能系统和物联网应用,FPMA提高了情绪分析的可靠性.
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