基于MIBE-RoBERTa-FF-BiLSTM的视频丹麦人的情绪分析
Jianbo Zhao1, Huailiang Liu2, Yakai Wang2
1School of Economics and Management, Xidian University, 266 Xifeng Road, Xi'an, 710071, China. zhaojianbo@stu.xidian.edu.cn.
这项研究引入了一种新的视频danmaku情绪分析方法,MIBE-RoBERTa-FF-BiLSTM,通过结合来自相互信息和分支的域特定词汇来实现高准确性. 该模型在分类danmaku情绪方面表现出卓越的性能,增强了视频内容分析.
科学领域:
- 自然语言处理自然语言处理.
- 情感计算是一种情感计算.
- 多媒体分析分析.
背景情况:
- 丹马库斯,即用户生成的评论,为视频平台提供实时的观众互动和情绪洞察,这对视频平台至关重要.
- 传统的情感分析与danmaku的独特特征作斗争,包括低可转移性,细分问题,注释不一致性和不充分的语义特征提取.
研究的目的:
- 开发一种先进的视频danmaku情绪分析方法,解决现有方法的局限性.
- 为了提高danmaku文本情感分类的准确性和稳定性.
- 创建一个专门的数据集和域名词典,用于danmaku情绪分析.
主要方法:
- 一种新的方法,MIBE-RoBERTa-FF-BiLSTM,被提出用于视频danmaku情绪分析.
- 一个"Bilibili必看清单和顶级视频丹麦人情绪数据集"是由10,000个丹麦人文本构建的.
- 使用相互信息 (MI) 和分支 (BE) 的新词识别算法识别了2610个新词,形成了一个域名词典.
- 马斯洛的需求等级理论指导着一致的情感注释.
- 域名词典被整合到RoBERTa-FF-BiLSTM模型中,以增强功能学习.
主要成果:
- 拟议的MIBE-RoBERTa-FF-BiLSTM模型在丹麦人情绪分类任务中获得了94.06%的优异F1得分.
- 对比实验证实了该模型的最佳综合性能,准确性和稳定性与主流方法相比.
- 集成的域名词典有效地增强了从danmaku文本中提取语义特征的功能.
结论:
- 在MIBE-RoBERTa-FF-BiLSTM方法显著提升视频danmaku情绪分析.
- 该研究强调了特定领域的词典和先进的深度学习架构对于分析用户生成内容的重要性.
- 未来的工作应该涉及手动词典构建,并纳入视频内容的更广泛的语义信息.
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