
9卷 第1期
基于BERT的股吧文本情感分类
基于BERT的股吧文本情感分类
- 2025年9卷第1期 页码:163-168
纸质出版日期:2025-01-30
DOI:10.12184/wspfzjjxWSP2515-823636.20250901
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9卷 第1期
成都信息工程大学 统计学院,四川 成都 610000
纸质出版日期:2025-01-30
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彭梓翛.基于BERT的股吧文本情感分类[J].发展经济学,2025,09(01):163-168.
彭梓翛. 基于BERT的股吧文本情感分类[J]. Development economics of china, 2025, 9(1): 163-168.
彭梓翛.基于BERT的股吧文本情感分类[J].发展经济学,2025,09(01):163-168. DOI: 10.12184/wspfzjjxWSP2515-823636.20250901.
彭梓翛. 基于BERT的股吧文本情感分类[J]. Development economics of china, 2025, 9(1): 163-168. DOI: 10.12184/wspfzjjxWSP2515-823636.20250901.
情感分类是自然语言处理(NLP)中的一个重要任务,其目标是通过分析文本的语言特征,识别其中的情感极性,通常将其划分为正向、负向和中立三类。本文探讨了基于BERT模型的股吧文本情感分类方法,旨在通过深度学习技术捕捉文本的深层语义信息,从而提高情感分类的准确性。首先,本文介绍了情感分类的背景和重要性,以及不同类型的情感分类方法,包括基于规则的、机器学习的和深度学习的方法。接着,详细描述了BERT模型的架构和其在情感分类中的应用,特别是通过多头注意力机制和自注意力机制捕捉文本的上下文信息。然后,本文展示了实验过程,包括文本数据的爬取、标注和模型的超参数设置。实验结果表明,微调后的BERT模型在股吧帖子情绪分类任务中表现优越,取得了显著的结果。与其他模型相比,BERT在所有指标上都有明显的优势,这表明微调后的BERT能够精准地识别股民情绪,对于投资者决策有重要参考价值。
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