利用混合模型对Twitter数据进行准确的情绪分析
Naga Surekha Jonnala1, A V S Ram Teja2, S Raja Rajeswari2
1Department of Electronics and Communication Engineering, NRI Institute of Technology, Agripalli, Eluru, AP, 521212, India.
Scientific reports
|July 8, 2025
概括
这项研究使用Twitter数据上的人工智能和自然语言处理来分析公众情绪. 联合Bi-LSTM和后勤回归模型在分类情绪方面取得了超过82%的准确性.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 社交媒体分析
背景情况:
- 情绪分析对于理解数字环境中的论至关重要.
- 推特是实时社交媒体参与和数据收集的关键平台.
研究的目的:
- 使用先进的人工智能技术,分析推特数据中的情绪.
- 评估Bi-LSTM和物流回归模型用于情绪分类的有效性.
主要方法:
- 使用自然语言处理 (NLP) 技术进行数据预处理:令牌化,停止词删除和干.
- 使用双向长短期存储器 (Bi-LSTM) 网络来捕获序列模式的特征表示.
- 使用优化超参数的逻辑回归模型进行情感分类.
主要成果:
- 综合方法实现了高性能指标:81.8%的精度,83.4%的回忆,82.5%的F1得分和82.32%的准确性.
- 证明了结合Bi-LSTM用于特征提取和物流回归用于分类的有效性.
- 从非结构化的Twitter数据中,成功地将情绪分类为积极或消极.
结论:
- 联合Bi-LSTM和物流回归模型为社交媒体情绪分析提供了强大的框架.
- 这种方法显示出在动态数字环境中增强情绪分类任务的巨大潜力.
- 突出了先进的人工智能和NLP在分析大规模文本数据以获得论洞察力的力量.
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