超越情感:一种算法策略,用于识别大文本体内的评价
Maximilian Overbeck1, Christian Baden1, Tali Aharoni1
1Department of Communication and Journalism, The Hebrew University of Jerusalem, Jerusalem, Israel.
Communication methods and measures
|February 27, 2025
概括
本研究引入了一种新的监督机器学习 (SML) 策略,用于识别文本中的对象特定评估. 该方法准确地对政治文本中的评价语言进行分类,优于现有的情绪分析工具.
科学领域:
- 计算语言学 计算语言学
- 自然语言处理自然语言处理.
- 政治科学 政治科学是指政治学.
背景情况:
- 传统的情绪分析很难将对象特定的评估与一般情绪区分开来.
- 识别评估表达式和评估对象之间的语义关系对于准确的分析至关重要.
- 现有的方法经常通过误解潜在的评估术语,产生错误的阳性结果.
研究的目的:
- 开发和评估一种新的监督机器学习 (SML) 策略,用于将特定对象的评估分类到大型文本体中.
- 应对确定术语与特定对象相关的评价功能的挑战.
- 提高政治话语中评估分类的准确性.
主要方法:
- 开发了一个监督机器学习 (SML) 分类器,以确定情感术语是否对一个对象进行评估.
- 该分类器在美国新闻媒体和政治家/记者推特的10004个文本段的集合体上进行了训练和测试.
- 重点是对有关2016年和2020年美国总统大选的政治预测的评估.
主要成果:
- 拟议的SML分类器显著优于现成的情绪分析工具和预先训练的基于变压器的情绪分类器.
- 该分类器在识别评估表达式方面表现出高度准确性,并正确地排除了许多非评估性使用情感术语.
- 这种虚假阳性减少提高了对象特定评估测量的可靠性.
结论:
- 与传统的情绪分析相比,开发的SML策略提供了更准确的方法来对文本中对象特定评估进行分类.
- 这种方法有助于在大规模的文本分析中更精确地测量评价,特别是在政治背景下.
- 未来的研究应该探索进一步改进和应用这种评估分类策略.
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