使用机器学习和深度学习模型进行情绪分析的VADER和TextBlob标签的比较:对生成AI用户体验的研究
Socheat In1, Sieb Chanchamnan2
1Department of Business Administration, Global Business School, Soonchunhyang University, Asan, Chungnam, Republic of Korea.
Acta psychologica
|January 22, 2026
概括
用户对像ChatGPT这样的生成AI的参与度随着实际实用性和情感联系而增长. 对88,343条评论的情绪分析表明,信任取决于内容质量,提高了人工智能采用率.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 自然语言处理自然语言处理.
背景情况:
- 生成性人工智能 (AI) 对于服务提供和用户参与越来越重要.
- 了解用户的感知和情感联系是人工智能采用的关键.
- 这项研究分析了使用生成性AI的用户体验,以确定接受的驱动因素.
研究的目的:
- 通过情绪分析,调查用户与生成AI的体验.
- 确定影响用户对人工智能的吸引力和信任的关键主题.
- 评估不同情绪分析工具和机器学习模型的有效性.
主要方法:
- 对88343个用户评论的情绪分析.
- 主题建模以确定关键主题.
- 使用VADER和TextBlob进行情感标签的情感分析.
- 机器学习 (GRU,SVM) 和深度学习分类器的性能评估.
主要成果:
- 吸引用户的关键主题包括创意产生,学习,支持和任务协助.
- 对ChatGPT的信任与感知到的内容质量和准确性有关.
- 标有TextBlob标签的数据集改善了分类器的性能,GRU获得了96.38%的准确性.
- 先进的机器学习和深度学习模型展示了卓越的预测性能.
结论:
- 用户对生成性AI的接受受实际实用性,情感参与和上下文相关性的影响.
- 基于词典的情感标签方法提高了分类的有效性.
- 人与人工智能的交互动态是由功能和情绪因素的组合形成的.
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