对BERT和FastText关于众筹活动成功预测的表述进行比较分析
1Software Engineering Department, Kocaeli University, Kocaeli, Marmara, Turkey.
PeerJ. Computer science
|September 24, 2024
概括
人工智能 (AI) 可以通过分析竞选文本来预测众筹成功. 像BERT和LSTM这样的先进AI模型显著提高了预测准确性,为投资者和企业家提供了有价值的见解.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 自然语言处理自然语言处理.
背景情况:
- 众筹是一种流行的融资方法,但许多活动都失败了.
- 减少参与众筹的风险对于投资者和企业家来说至关重要.
- 人工智能 (AI) 为预测众筹成功提供了潜在的解决方案.
研究的目的:
- 研究先进的人工智能技术在预测Kickstarter众筹活动成功方面的有效性.
- 为了比较BERT和FastText文本表示模型的性能.
- 评估LSTM和GBM分类器在与这些模型相结合时的预测能力.
主要方法:
- 预处理众筹活动的简报.
- 使用BERT和FastText提取文本特征.
- 使用LSTM和GBM分类器评估预测性能.
- 为特定领域的应用程序微调预训练模型.
主要成果:
- 伯特表示显著超过了快速文本.
- 使用微调的BERT模型与LSTM相结合,获得了0.745的最高精度.
- 拟议的方法显示出优于现有方法的优势.
结论:
- 深度的上下文嵌入,特别是来自微调的BERT模型,对于准确的众筹成功预测至关重要.
- 由人工智能驱动的预测模型为活动创建者和投资者提供了实际影响.
- 这项研究为改善众筹领域的预测模型提供了宝贵的见解.
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