社交媒体网络公众论情绪分类方法基于多特征融合和多规模混合神经网络
Yuan Yao1, Xi Chen2, Peng Zhang3
1College of Humanities and Law, Harbin University, Harbin, China.
PeerJ. Computer science
|February 3, 2025
概括
这项研究引入了一种新的多功能融合词向量模型 (WOOSD-CNN),用于增强情绪分析. 该模型提高了分类情绪极性和社交媒体文本分类有效性的准确性.
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:
- 社交媒体产生了大量用户生成的内容 (UGC) 与主观的意见.
- 分析这篇文章可以了解公众情绪,市场趋势和行业动态.
- 准确的情绪分析对于及时进行战略调整至关重要.
研究的目的:
- 开发一个改进的词向量模型,用于对社交媒体文本的情感分析.
- 为了提高情绪极性准确性和分类有效性.
- 提出一个统一的学习框架,用于UGC的层面情绪分析.
主要方法:
- 使用语义融合和单词顺序特征构建的矢量.
- 基于单词相似性开发了一个词汇向量,并监督了 corpora 培训.
- 通过连接加权转移向量,形成了一个多功能融合的情感词向量.
- 提出了一个统一的学习框架,并提供了一个信息交互道,用于面向层面的情绪分析.
- 引入了位置感知模块,以解决标签漂移和地方发展计划 (LDP) 问题.
主要成果:
- 多特征融合 (WOOSD-CNN) 模型在情绪极性准确度方面表现出显著的改进.
- WOOSD-CNN模型在多类微博评论数据集上显示了增强的分类效率.
- 拟议的统一学习框架有效地利用了潜在的交互性文本特征来进行层面情绪分析.
结论:
- 多功能融合方法显著提高了情绪分析的性能.
- 开发的模型为分析用户生成内容中的情绪提供了有效的解决方案.
- 未来的工作可以探索在层面情绪分析框架的进一步改进.
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