主题发现和热点分析情绪分析使用信息理论方法分析中文文本
Changlu Zhang1,2, Haojie Fan2,3, Jian Zhang1,2
1School of Economics & Management, Beijing Information Science & Technology University, Beijing 100192, China.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
这项研究引入了一种新的模型,用于发现文本情绪分析中的研究趋势. 关键发现显示,社交媒体意见分析是一个热门话题,突出了方法集成的必要性,并解决了面向层面分析的挑战.
科学领域:
- 计算机科学 计算机科学
- 统计科学 统计科学
- 自然语言处理自然语言处理.
背景情况:
- 情绪分析是一个快速发展的研究领域,在各种科学学科中具有重大影响.
- 了解研究趋势对于学者来说至关重要,以导航文本情感分析文献的动态景观.
研究的目的:
- 在文本情感分析文献中提出和验证一个用于自动主题发现和趋势分析的新模型.
- 确定关键的研究主题,它们的演变,以及从2012年到2022年在该领域出现的新挑战.
主要方法:
- 使用FastText用于关键字矢量化和协同符号相似性用于同义词合并.
- 应用了对主题进行分类和信息获取以提取特征词语的等级分类,并使用了Jaccard系数来进行分类.
- 进行时间序列分析并构建了一个四象限矩阵,以可视化不同时间阶段的主题分布和研究趋势.
主要成果:
- 在文本情绪分析文献中确定了12个不同的研究类别,从2012-2022年.
- 社交媒体 (例如微博) 的在线意见分析成为当前突出的研究重点.
- 在2012-2016年和2017-2022年期间观察到研究重点的重大转变.
结论:
- 拟议的模型有效地识别和分析文本情绪分析的研究趋势.
- 未来的研究应该优先考虑整合情绪词典,传统机器学习和深度学习方法.
- 在面向层面的情绪分析中解决语义歧义问题,并推进多式联运/跨式联运情绪分析是关键的未来方向.
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