相关实验视频
情绪分析中不断发展的技术:综合性综述
Mahander Kumar1, Lal Khan2, Hsien-Tsung Chang3,4,5
1Department of Computer Science, Mir Chakar Khan Rind University, Sibi, Balochistan, Pakistan.
PeerJ. Computer science
|February 17, 2025
概括
情绪分析技术正在发展,以处理来自社交媒体的大量用户生成的内容. 本综述详细介绍了从机器学习到BERT和GPT等先进的变压器模型的方法,有助于未来的研究.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 计算社会科学 计算社会科学
- 人工智能 (AI) 是一种人工智能.
背景情况:
- 社交媒体和电子商务平台上用户生成内容的爆炸性增长.
- 分析庞大的,非结构化的公众情绪数据的挑战.
- 需要在文本中自动检测意见和情绪.
研究的目的:
- 综合审查不断发展的情绪分析技术.
- 为了比较古典和现代的方法,包括变压器模型.
- 识别当前的趋势,未来的方向和现场挑战.
主要方法:
- 检查基础过程:数据收集和特征提取.
- 评论词嵌入技术和机器学习算法.
- 对上下文嵌入和先进的变压器模型 (GPT,BERT,T5) 的分析.
主要成果:
- 详细比较各种情绪分析方法.
- 强调不同技术的适当用途和局限性.
- 综合当前趋势和未来的研究途径.
结论:
- 情绪分析对于从在线数据中了解公众情绪至关重要.
- 像BERT和GPT这样的先进模型比传统方法提供了显著的改进.
- 该审查为评估当前状态和指导情绪分析未来进步提供了基础.
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