通过整合上下文意识注意力和融合网络来进行刺检测的增强语义表示学习
Shufeng Hao1,2, Jikun Yao3, Chongyang Shi4
1College of Data Science, Taiyuan University of Technology, Taiyuan 030024, China.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
本研究介绍了一个上下文刺检测模型 (CSDM),以改善社交媒体上的刺检测. 该模型通过结合用户配置文件和论坛主题来提高准确性,优于现有的方法.
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
- 人工智能的人工智能
背景情况:
- 刺在社交媒体上很常见,使情绪分析变得复杂.
- 传统方法专注于文本内容,忽视了上下文线索.
- 准确的刺言论检测对于理解用户情绪至关重要.
研究的目的:
- 开发一种先进的模型来检测在线文本中的刺言论.
- 为了提高准确性,利用文本信息超越句子内容.
主要方法:
- 提出了一个上下文刺检测模型 (CSDM).
- 采用了一个Bi-LSTM编码器,对上下文有意识的注意力,以实现精致的评论表示.
- 利用用户论坛融合网络来整合用户配置文件和论坛主题信息.
主要成果:
- 在不同的数据集上,CSDM实现了0.69,0.70和0.83的精度.
- 在一个大型的Reddit群体 (SARC) 上,与最先进的方法相比,表现出显著的性能改善.
结论:
- 包括用户配置文件和论坛主题在内的上下文线索对于有效检测刺言论至关重要.
- 拟议的CSDM提供了一种更全面的方法来了解在线情绪.
- 这个模型推进了自动刺言论检测领域.
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