基于文本语义增强的暗示词的情感识别和应用的研究
Xiaomin Li1,2, Hao Wang1,2, Bin Shi1,2
1School of Information Management, Nanjing University, Nanjing, China.
PloS one
|November 4, 2024
概括
本研究介绍了一种自动计算模型,用于识别暗示情绪,增强人文知识发现. ERNIE-RCNN模型显著提高了准确性,使得组织和关联暗示资源的新方法成为可能.
科学领域:
- 数字人文学科 数字人文学科
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
背景情况:
- 传统的人文科学研究方法限制了暗示性资源的分析.
- 在应用自动计算技术来理解暗示中的情绪方面存在差距.
- 数字智能为数据驱动的人文知识组织提供了新的机会.
研究的目的:
- 提出和评估一种用于暗示性词语情感识别和应用的新型模型.
- 利用文本语义增强来改善暗示的情感分析.
- 开发一个数据库,用于语义关联和组织暗示相关的人文知识.
主要方法:
- 使用36,080个含义词的解释文本来增强文本的语义.
- 深度学习模型的比较,包括ERNIE-RCNN,用于情绪预测.
- 情绪分布分析和源文情绪推断.
- 隐藏的迪里克莱特分配 (LDA) 用于主题提取和与情绪数据的融合.
主要成果:
- 在ERNIE-RCNN模型中,暗示性词语情绪识别的准确性提高了6.35%.
- 识别了暗示性词语情绪的总体和基于时间的分布规律.
- 成功推断出源文本的情绪,基于暗示性的词情绪.
- 构建了一个暗示性词语-情感主题关系数据库.
结论:
- 拟议的模型有效地增强了文本语义,以准确地识别暗示性词语情感.
- 开发的数据库有助于人类知识的新意义关联和组织.
- 自动计算技术显示出在数字人文学中推进暗示研究的巨大潜力.
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