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与COP9相关的推特的情绪分析:对预训练模型和传统技术的比较研究

Sherif Elmitwalli¹, John Mehegan¹

¹Tobacco Control Research Group, Department for Health, University of Bath, Bath, United Kingdom.

Frontiers in big data

|April 4, 2024

概括

这项研究比较了情绪分析技术,发现BERT和GPT-3在标准数据集上表现出色. GPT-3在COP9专用推特上表现出卓越的表现,为有限数据场景提供了有价值的工具.

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:

在NLP中,情绪分析对于理解公众论至关重要.
传统方法包括基于词典和机器学习的方法.
预先训练的模型,如BERT和GPT-3,提供先进的功能.

研究的目的:

为了比较各种情绪分析技术.
为了评估标准数据集 (IMDB,Sentiment140) 和特定领域 (COP9推特) 的性能.
确定分析具有有限注释的域特定数据的最有效技术.

主要方法:

采用了两阶段的评估过程.
第一个阶段:对基于词典的机器学习,Bi-LSTM,BERT和GPT-3在标准数据集上的比较分析.
第二阶段:将表现最好的模型应用于部分注释的COP9推文.

主要成果:

在标准数据集上,BERT获得了最高的F1分数 (IMDB:0.9380,感觉140:0.8114).
在标准数据集上,GPT-3紧随其后 (IMDB: 0.9119,Sentiment140: 0.7913).
在COP9推特上,GPT-3获得了最佳表现 (F1得分:0.8812).

结论:

预先训练的模型 (BERT,GPT-3) 在情绪分析中表现优于传统方法.
GPT-3显示了对具有有限注释的域特定数据的强烈概括.
预先训练有素的模型是有效的情绪分析在低资源或新领域.

关键词:

贝尔特 (BERT) 公司这是一个双LSTM.第九届COP9会议在 GPT-3 中使用.在LLMS中,LLMS是最重要的.基于词典的词典.情绪分析是一种情绪分析.

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