跨语言情绪分析的多模式方法,使用变压器和LLM的合奏
Md Saef Ullah Miah1, Md Mohsin Kabir2, Talha Bin Sarwar1
1Department of Computer Science, American International University-Bangladesh, Dhaka, 1229, Bangladesh.
Scientific reports
|April 26, 2024
概括
这项研究引入了一种用于外语情绪分析的新型组合模型,通过将文本翻译成英语来实现超过86%的准确性. 该方法有效地克服了在自然语言处理中有限的标记数据所带来的挑战.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 机器学习 机器学习
背景情况:
- 情绪分析对于了解各个领域的论至关重要.
- 由于缺乏标记的数据集,在外语中分析情绪是具有挑战性的.
- 现有的方法在没有广泛的语言特定培训的情况下,在多语言情绪检测方面扎.
研究的目的:
- 开发一种有效的外语情绪分析方法.
- 利用翻译和集合建模来克服数据限制.
- 为了评估组合变压器和大型语言模型 (LLM) 方法的性能.
主要方法:
- 提出了一个组合模型,结合了变压器和一个大型语言模型 (LLM).
- 四种语言 (阿拉伯语,中文,法语,意大利语) 用LibreTranslate和Google Translate被翻译成英语.
- 使用一组预先训练的模型进行了情感分析:Twitter-Roberta-Base-Sentiment-Latest,bert-base-多语种未婚情感,以及GPT-3.
主要成果:
- 拟议的整体模型在翻译外语文本的情感分析中取得了超过86%的准确性.
- 与个人预训练模型和LLM相比,整体方法显示出更高的性能.
- 翻译成英语被证明是外国语言情绪分析的有效策略.
结论:
- 外语情绪分析是通过翻译成英语等基础语言而可行和准确的.
- 集成建模,整合变压器和LLM,增强情绪分析的稳定性和准确性.
- 拟议的方法为低资源语言的情感分析提供了可行的解决方案.
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