在Twitter上使用机器学习对汽车品牌感知分析量化客户情绪
Sujith Samuel Mathew1, Kadhim Hayawi2, Neethu Venugopal1
1College of Interdisciplinary Studies, Zayed University, Abu Dhabi, United Arab Emirates.
Scientific reports
|January 19, 2026
概括
这项研究引入了一个品牌两极分数 (BPS),使用推特数据的情绪分析来追踪客户对汽车品牌的看法. 该BPS提供了对品牌定位和情绪动态的实时洞察.
科学领域:
- 社交媒体分析.
- 品牌管理 品牌管理
- 自然语言处理自然语言处理.
背景情况:
- 社交网络网站对公众论和品牌认知至关重要.
- 品牌经理们利用这些平台来获得消费者洞察力.
- 了解客户情绪对于品牌战略至关重要.
研究的目的:
- 开发一种量化衡量消费者对汽车品牌情绪的方法.
- 通过对Twitter数据的情绪分析来评估品牌认知.
- 引入一个加权的"品牌两极分数" (BPS),用于动态的品牌监控.
主要方法:
- 对五个领先的汽车品牌的Twitter (或X) 数据进行情绪分析.
- 发展品牌两极性评分 (BPS) 模型.
- 根据推特的影响力 (参与度指标,作者追随者数) 权衡BPS.
主要成果:
- BPS有效量化了对品牌的消费者情绪 (积极/消极).
- 该分数提供了近乎实时的品牌定位和情绪跟踪.
- 验证通过各种评估证实了BPS系统的稳定性.
结论:
- 拟议的品牌两极分数 (BPS) 是监测品牌认知的一个有价值的工具.
- BPS促进了进步和竞争性的品牌分析.
- 该系统有助于全面了解数字时代的品牌动态.
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