情绪分析:全面的审查,最近的进展和公开的挑战
概括
本研究提供了情绪分析 (SA) 的全面概述,重点关注多式联络方法. 它详细介绍了单模SA方法,并引入了多模SA (MSA) 的新分类法,以指导未来的研究.
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.
- 计算机科学 计算机科学
背景情况:
- 情绪分析 (SA) 已经取得了显著的进步,在论和语音服务中得到了应用.
- 深度学习刺激了多式联运SA (MSA) 的研究,但缺乏全面的调查.
- 现有的研究往往单独分析单个模式,阻碍了对MSA的整体理解.
研究的目的:
- 系统地审查和分类现有的SA方法,重点是多式联络方法.
- 基于数据模式,为了解SA任务提供一个框架.
- 确定MSA的研究缺口,并提出未来的方向.
主要方法:
- 提出了一个新的框架,按模式组织SA任务.
- 详细审查单模SA工作流程,数据处理和建模.
- 引入MSA的新分类学,涵盖表示学习和数据融合.
主要成果:
- 综合分析单一模式的SA,突出相似之处和差异,以告知MSA.
- 详细讨论了多式联络表示学习和数据融合技术.
- 探索先进的主题,如多式联络对齐,聊天机器人,和ChatGPT在SA.
结论:
- 这篇文章提供了SA和MSA的结构化概述,解决了当前的研究缺口.
- 确定了未来研究的关键领域,包括跨模式的对比学习和预培训模型.
- 为研究人员提供有关各种SA任务的数据集和方法的指导.
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