使用基于RoBERTa的混合模型改进情绪分类
Noura A Semary1, Wesam Ahmed1,2, Khalid Amin1
1Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shibin El Kom, Egypt.
Frontiers in human neuroscience
|December 22, 2023
概括
这项研究介绍了一种混合深度学习模型,将RoBERTa,CNN和LSTM结合起来,用于增强情绪分析. 该模型在电影和Twitter评论数据集上取得了高准确性,超过了标准方法.
科学领域:
- 自然语言处理自然语言处理.
- 深度学习 (Deep Learning) 是一种深度学习.
- 机器学习 机器学习
背景情况:
- 传统的情感分析方法面临着复杂的语言细微差别带来的挑战.
- 现有的深度学习模型在捕捉上下文语义方面存在局限性.
研究的目的:
- 开发一种混合深度学习模型,以改善情绪分类.
- 利用变压器和序列模型的优势,同时减轻它们的弱点.
主要方法:
- 一个混合模型,将强大的优化BERT (RoBERTa) 与卷积神经网络 (CNN) 和长短期记忆 (LSTM) 集成在一起.
- 使用RoBERTa进行句子向量表示,并使用CNN+LSTM进行语义理解.
- 使用SMOTE技术与词嵌入来解决Twitter数据集中的阶级不平衡.
主要成果:
- 在IMDb电影评论数据集上获得了96.28%的准确性.
- 在美国航空公司的Twitter评论数据集上获得了94.2%的准确性.
- 与标准情绪分析技术相比,表现优越.
结论:
- 拟议的混合 RoBERTa- ((CNN+LSTM) 模型对于情绪分类非常有效.
- 这种方法成功地提高了对文本数据中的语义和上下文的理解.
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