多领域适应性学习领域的多领域情绪分类大数据上的情绪分类
Maha Ijaz1, Naveed Anwar1, Mejdl Safran2
1Department of Computer Science Faculty of Computing and Information Technology University of Gujrat, Gujrat, Pakistan.
PloS one
|April 1, 2024
概括
转移学习通过使模型能够从一个领域的标记数据中学习并将其应用于另一个领域的未标记数据来改进情绪分析. 这种方法提高了性能,特别是在大数据挑战和有限的标记数据集的情况下.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 计算语言学 计算语言学
背景情况:
- 传统的情绪分析模型与特定领域的细微差别和大型数据集作斗争.
- 监督学习需要大量的,耗时的数据标签,往往导致数据集不足.
研究的目的:
- 评估转移学习在多域情感分类 (MDSC) 中的有效性.
- 评估域调整对情绪分析表现的影响.
- 量化增强转移学习带来的情感分析结果.
主要方法:
- 使用的转移学习模型:BERT,RoBERTa,ELECTRA,以及ULMFiT.
- 采用多领域情感分类 (MDSC) 技术进行跨领域学习.
- 将变压器模型与LSTM和CNN架构进行比较.
主要成果:
- 转移学习模型在不同领域的情绪分析中表现得更好.
- 使用转移学习的域名适应有效地解决了未标记的目标域名中的挑战.
- 在五个数据集 (酒店评论,电影评论,推特,CSC,BCC) 上的实验证实了模型的有效性.
结论:
- 转移学习显著增强情绪分析,特别是在有限的标记数据和多种领域的场景中.
- MDSC技术为跨领域情绪分类提供了一个强大的解决方案.
- 基于变压器的模型,通过转移学习来增强,在情感分析任务中表现优越.
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