机器学习和词汇基于规则的成本效益高的情感注释 Hinglish 发言的注释
Pratibha Verma1, Amandeep Kaur2, Meenu Khurana3
1Chitkara University Institute of Engineering & Technology, Chitkara University; pratibha@chitkara.edu.in.
Journal of visualized experiments : JoVE
|September 8, 2025
概括
这项研究介绍了一种混合主动学习框架,用于Hinglish文本中的情感注释. 它通过结合规则,机器学习和专家反来实现高精度和降低成本.
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.
- 情感计算是一种情感计算.
背景情况:
- 像英语这样的代码混合语言中的情感注释面临着由于语言复杂性和有限资源的挑战.
- 现有的方法经常与英语的细微差别作斗争,影响准确性和成本效益.
研究的目的:
- 为英语文本开发一个经济高效,高准确度的情感注释框架.
- 整合词汇规则,机器学习和主动学习,以改善注释.
- 为了应对代码混合语言中情感分析的独特挑战.
主要方法:
- 一个混合的积极学习框架,结合了词汇规则,机器学习和代的专家反.
- 利用双语情感字典,复合术语的子词标记化,以及样本优先级的积极学习.
- 基于离散情绪理论和认知评估理论.
主要成果:
- 在与战争和冲突有关的19,000条Hinglish推文数据集上,获得了81%的准确性 (F-score:0.76).
- 与传统的手动注释相比,运营成本降低了40%.
- 词汇规则解决了89%的代码交换模两可,代改进将准确度从72%提高到81%.
结论:
- 混合框架显著提高了Hinglish中情感注释的准确性,同时降低了成本.
- 将基于规则的方法与主动学习和机器学习相结合,对于代码混合语言处理是有效的.
- 对表情符号,标签和语的自动预处理有助于提高系统效率.
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