从变压器 (BERT) 模型中使用双向编码器表示来进行情感分类的转移学习
Ali Areshey1, Hassan Mathkour1
1Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了基于BERT的情绪分析模型,实现了在线评论分类的卓越预测和准确性. 这项研究强调了转移学习对改善情绪分析概括性的有效性.
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:
- 情绪分析对于推系统和理解在线用户意见至关重要.
- 现有的方法通常依赖于手动功能工程和浅层学习,限制了概括.
- 关于预测审查有用性的各种方法的有效性,存在相互矛盾的结果.
研究的目的:
- 通过转移学习开发一种通用的情感分析方法.
- 应用和评估基于变压器 (BERT) 的双向编码器表示模型.
- 将BERT分类与传统机器学习技术的性能进行比较.
主要方法:
- 使用基于BERT的模型进行情绪分类.
- 员工转移学习以增强模型的概括性.
- 进行了对Yelp评论的比较实验,将BERT与其他机器学习方法进行了评估.
- 研究了批量大小和序列长度对BERT分类器性能的影响.
主要成果:
- 拟议的BERT模型表现出卓越的性能,实现了高精度和出色的预测.
- 精心调整的BERT分类在积极和消极的Yelp评论方面表现优于其他方法.
- 批量大小和序列长度被确定为影响BERT分类性能的重要因素.
结论:
- 基于BERT的转移学习为情绪分析提供了更广泛,更有效的方法.
- 该模型显示了与传统方法相比的显著改进,用于分类在线评论情绪.
- 进一步的研究应考虑批量大小和序列长度等超参数对最佳BERT性能的影响.
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