UsbVisdaNet:用户行为视觉蒸和注意力网络,用于多模式情感分类.
Shangwu Hou1, Gulanbaier Tuerhong1, Mairidan Wushouer1
1Xinjiang Multilingual Information Technology Laboratory, Xinjiang Multilingual Information Technology Research Center, College of Information Science and Engineering, Xinjiang University, Urumqi 830017, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究引入了UsbVisdaNet,通过分析心理行为来检测有偏见的用户评论. 这种方法通过在多式联络数据中识别两极分化的意见,提高了情绪分析的准确性.
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.
- 计算机视觉 计算机视觉
背景情况:
- 偏见的用户评论会对公司的评价产生负面影响,并可能传播错误信息.
- 识别具有心理偏见的用户对于准确的情绪分析至关重要.
- 现有的方法可能无法完全解决多式联络审查中主观偏见的细微差别.
研究的目的:
- 提出一种用于多式联运数据情绪分类的新方法.
- 通过分析他们的心理行为来识别有偏见的用户.
- 通过减轻主观偏见来提高情绪分类的准确性.
主要方法:
- 开发了UsbVisdaNet (用户行为视觉蒸和注意网络) 用于多式联络情绪分类.
- 在多个层次层次上集成用户行为,文本和图像功能.
- 利用心理行为分析来检测正面和负面偏见的用户.
主要成果:
- UsbVisdaNet在Yelp多式联络数据集上展示了优越的情绪分类性能.
- 该方法有效地识别了有偏见的用户,提高了整体情绪分析的准确性.
- 废除和比较实验验证实了拟议方法的有效性.
结论:
- UsbVisdaNet成功地集成了多式联网功能,用于增强情绪分析.
- 分析用户行为是检测和减轻审查偏见的关键.
- 这项研究在理解和分类两极分化的观点方面取得了重大进展.
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