对表情符号的情感分析使用机器学习和伯特的综合评论
Amit Khan1, Dipankar Majumdar2, Bikromadittya Mondal3
1Department of Information Technology, RCC Institute of Information Technology, Kolkata, 700015, India. amit.khan@rcciit.org.in.
Scientific reports
|March 4, 2025
概括
这项研究引入了一种新的情感分析方法,该方法有效地结合了表情符号和表情符号. 新方法显著提高了处理客户反以实现业务增长的准确性.
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.
背景情况:
- 客户反分析对于航空业的业务增长至关重要.
- 表情符号和表情符号传达了重要的用户情绪,这在纯文本分析中经常被忽视.
- 当前的方法在数据预处理过程中经常排除表情符号/情形符号,从而限制了情绪分析的准确性.
研究的目的:
- 开发一种新的情感分析方法,有效地利用表情符号和表情符号.
- 通过包括这些视觉线索来提高情感分类的准确性.
- 解决传统数据预处理中排除表情符号/情形符号的限制.
主要方法:
- 使用自然语言处理 (NLP) 和人工智能 (AI).
- 开发了一种用于情感分析的新方法,结合了表情符号和表情符号.
- 在美国航空公司推特数据集上应用机器学习分类器和BERT模型.
主要成果:
- 在情感分析中达到92%的准确性.
- 与现有的最先进的框架相比,表现优越,性能优于它们9%.
- 通过包含表情符号和表情符号,有效地捕捉到微妙的情绪.
结论:
- 拟议的模型通过结合表情符号和表情符号,在情感分析方面取得了重大进展.
- 这种方法提高了对航空行业客户意见和经验的理解.
- 这些发现强调了包括所有形式的用户表达对于准确的反分析的重要性.
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