研究注意力的情感分析:概念的Altmetric证明
Carlos Areia1,2, Michael Taylor1,3, Miguel Garcia1
1Digital Science, London, United Kingdom.
Frontiers in research metrics and analytics
|November 17, 2025
概括
这项研究引入了一种人工智能框架,用于对X (以前的Twitter) 上研究提及的情感分析. 与传统方法相比,人工智能模型显著改善了对公众研究接收的理解.
科学领域:
- 图书识别和Altmetrics可以使用.
- 人工智能在研究评估中的作用
- 社交媒体分析 社交媒体分析
背景情况:
- 传统的文献计量 (例如,引用数) 无法捕捉定性研究的影响.
- 高度指标提供更广泛的覆盖范围,但往往缺乏情绪分析.
- 了解公众对研究的情绪对于影响评估至关重要.
研究的目的:
- 开发和评估人工智能驱动的情绪分析框架,用于社交媒体上的研究提及.
- 评估科学成果在线讨论背后的语气和意图.
- 用微妙的情绪数据来增强高度分析.
主要方法:
- 开发了一个七级情绪分类系统 (强烈负面到强烈积极).
- 使用大型语言模型 (LLM) 来进行情感分类,通过专家评估进行精细化.
- 使用F1分数,与基线机器学习模型 (ML2024) 进行LLM性能比较.
主要成果:
- 基于LLM的情绪分析模型获得了F1得分0.577,超过了基线ML2024模型 (F1得分=0.419).
- 由人工智能驱动的方法在情绪分类中表现出更好的精度和回忆.
- 该模型显示了与人类对研究提及情绪的评估的加强对齐.
结论:
- 先进的人工智能,特别是LLM,可以通过结合情绪来显著增强高度分析.
- 这个框架提供了一个更丰富的,对研究在社交媒体上的接收有意识的理解.
- 这些发现为将情绪分析整合到altmetric平台铺平了道路,以获得更深入的社会话语见解.
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