基于改进的BERT模型对直播电子商务评论的文本分类算法的研究
Rong Zhou1, Qing Shen2, Huafeng Kong2
1Faculty of Business and Economics, University of Malaya, Kuala Lumpur, Malaysia.
PloS one
|April 22, 2025
概括
本研究介绍了一种层次的BERT模型,用于对电子商务实时流评论进行分类. 该模型提高了分析这些有价值,大量客户互动的准确性和效率.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 电子商务分析 电子商务分析
背景情况:
- 电子商务直播产生了大量简短,多样化的"子弹评论".
- 分析这些评论对于理解客户参与和营销效率至关重要.
- 现有的方法难以应对子弹评论数据的数量和复杂性.
研究的目的:
- 开发一种改进的电子商务分类模型.
- 为了提高子弹评论分析的准确性和效率.
- 为营销目的而提取有价值的信息.
主要方法:
- 提出了一种改进的BERT模型,使用层次分类结构.
- 训练了一个父类BERT模型用于广泛分类.
- 开发了BERT模型的子类别,用于在类别内细粒度分类.
主要成果:
- 层次的BERT模型显著提高了分类准确性.
- 该模型在处理大量评论时表现出更高的效率.
- 经验证据证实了该模型的有效性.
结论:
- 层次化的BERT方法为电子商务公开评论分析提供了强大的解决方案.
- 这种方法有助于从实时流互动中提取可操作的见解.
- 改进的分析支持电子商务中更有效的营销策略.
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