使用ESN ISPBO和BERT的整合进行推特情绪分析的混合方法
Zhaojia Chai1, Nan Sun2, Qingyang Zhang3
1Institute of Language Sciences, Shanghai International Studies University, Shanghai, 201620, China.
Scientific reports
|December 17, 2025
概括
这项研究引入了推特情绪分析的新框架,结合了回声状态网络 (ESN),改进的基于学生心理学的优化 (ISPBO) 和BERT嵌入,以提高意见挖掘的准确性.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 情感分析对于从文本中提取主观见解至关重要.
- 对社交媒体数据,特别是Twitter的有效情绪分析仍然是一个挑战.
- 现有的模型经常与实时社交媒体数据的细微差别和数量作斗争.
研究的目的:
- 引入一个创新的框架,用于推特情绪分析.
- 为了提高从社交媒体文本中提取情绪的准确性和效率.
- 为了证明混合方法的有效性,将ESN,ISPBO和BERT整合在一起.
主要方法:
- 开发了ESN-ISPBO-BERT模型,集成了回声状态网络 (ESN),改进了基于学生心理学的优化 (ISPBO) 和BERT嵌入.
- 在基准数据集上评估模型:SemEval-2016-1,SemEval-2016-2和斯坦福情绪树库 (SST-2).
- 将拟议的模型与各种基线方法进行比较,包括SVM-Glove,CNN-BERT,LSTM,CNN,KNN,SVM,BERT和GRU.
主要成果:
- 在评估的数据集上,ESN-ISPBO-BERT模型显著超过了所有基线模型.
- 在SemEval-2016-2上实现了高性能指标 (98.82%的准确性,98.79%的精度,98.96%的回忆,98.87%的F1得分).
- 在SemEval-2016-1 (98.76%准确率,98.81%准确率,98.92%回忆率,98.86%F1得分) 和SST-2 (98.51%准确率,98.42%准确率,98.87%回忆率,98.64%F1得分) 上表现出卓越的结果.
结论:
- 集成水库计算 (ESN),高级优化 (ISPBO) 和上下文嵌入 (BERT) 是非常有效的情绪分析.
- 拟议的混合模型为分析Twitter数据和了解公众意见提供了一个强大的解决方案.
- 这项研究强调了混合策略在推进社交媒体应用程序 (如品牌监控和客户反分析) 的情绪分析方面的重要性.
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