基于变压器和变压器前模型的各种嵌入式情绪预测:亚马逊评论的案例研究
Ismail Duru1, Ayşe Saliha Sunar2,3
1R&D Department, Türk Telekom, Ankara 06103, Turkey.
Entropy (Basel, Switzerland)
|December 24, 2025
概括
这项研究比较了情绪分析模型,发现像DistilBERT这样的基于变压器的方法提供了更高的准确性和信心. 在各种模型中,FastText嵌入也表现出强的性能,特别是在回忆方面.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 情绪分析对于消费者洞察至关重要,但面临着模两可的语言和各种数据的挑战.
- 为情绪分类选择最佳模型和嵌入仍然很困难.
研究的目的:
- 在传统,深度学习和变压器范式中全面评估情绪分类模型.
- 为了比较静态 (GloVe,FastText) 和上下文 (BERT,DistilBERT) 的嵌入.
- 为模型选择引入一个对意识的框架.
主要方法:
- 机器学习,深度学习 (LSTM) 和变压器模型的比较分析.
- 在亚马逊数据集上对静态和变压器嵌入的评估.
- 使用准确度,精度,回忆,F1得分和交叉来测量性能.
- 对错误分类的样本进行定性分析.
主要成果:
- 由于子词信息,FastText嵌入的性能优于GloVe,特别是在回忆中.
- 变压器模型,特别是DistilBERT,实现了最高的精度 (92%) 和最低的交叉 (0.25).
- 结果在第二个数据集上验证,显示一致的趋势.
结论:
- 变压器模型,特别是DistilBERT,提供了精准和准确的情绪预测.
- 拟议的意识框架支持知情,情境敏感的模型选择.
- 这些发现有助于推进情绪分析和可持续的AI实践.
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