通过组合主题建模和情绪分析,创新地挖掘用户需求:汽车案例研究
Yujia Liu1, Dong Zhang2, Qian Wan1
1School of Art and Design, Guangdong University of Technology, Guangzhou 510090, China.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
这项研究引入了一种新方法,结合主题建模和情绪分析,从社交媒体数据中挖掘用户需求. 它帮助汽车公司了解不断变化的客户需求,以更好地开发产品.
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.
- 数据挖掘 数据挖掘
背景情况:
- 汽车行业的用户需求正在迅速发展.
- 挖掘用户生成的内容 (UGC) 是了解动态需求的关键.
- 现有的UGC分析单一方法方法存在局限性.
研究的目的:
- 提出一种用于用户需求挖掘的新方法.
- 结合主题建模和情绪分析,进行全面的用户需求分析.
- 为汽车制造商提供可操作的见解.
主要方法:
- 使用BERTopic从UGC提取主题.
- 雇佣了罗伯塔来分析UGC的情绪.
- 综合主题建模和情绪分析,用于链接分析.
主要成果:
- 用户要求分为性能,舒适性,价格敏感性和安全性.
- 识别了传感器和动力系统等先进功能越来越重要.
- 通过结合方法证明了通过结合方法提高用户需求的识别.
结论:
- 这种新方法提供了对用户需求的更全面的分析.
- 结果为汽车产品优化和营销提供了可操作的见解.
- 这种方法是可扩展的,可以适应各种行业.
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