使用深度学习方法进行阿姆哈拉语政治情绪分析
Fikirte Alemayehu1, Million Meshesha2, Jemal Abate3,4
1Department of Information Science, Haramaya University, Dire Dawa, Ethiopia.
Scientific reports
|October 20, 2023
概括
这项研究引入了一种混合深度学习模型用于阿姆哈拉语政治情绪分析,达到91.60%的准确性. 未来的工作包括扩大数据集和探索微妙的情绪,如刺.
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
- 人工智能的人工智能
背景情况:
- 情绪分析对于了解社交媒体上的公众意见至关重要.
- 由于资源有限和语言细微差别,阿姆哈拉语情绪分析面临着挑战.
- 社交媒体上的政治话语需要准确的情感分类来进行知情分析.
研究的目的:
- 开发和评估阿姆哈拉语政治情绪分析的深度学习模型.
- 为了比较卷积神经网络 (CNN),双向长期短期记忆 (Bi-LSTM) 和混合CNN-Bi-LSTM模型的性能.
- 强调对阿姆哈拉语语言的先进情绪分析技术和标准化数据集的需求.
主要方法:
- 使用深度学习技术:卷积神经网络 (CNN) 和双向长期短期记忆 (Bi-LSTM).
- 开发并测试了一种混合CNN-Bi-LSTM模型,用于增强情绪分类.
- 将模型应用于从埃塞俄比亚社交媒体平台提取的政治句子.
主要成果:
- 混合CNN-Bi-LSTM模型实现了最高准确率的91.60%.
- 演示了深度学习方法对阿姆哈拉语情绪分析的有效性.
- 确定了包括数据集大小在内的局限性,以及检测刺等微妙情绪的挑战.
结论:
- 混合CNN-Bi-LSTM模型为阿姆哈拉语政治情绪分析提供了强大的解决方案.
- 建议过渡到多类分类,以更详细地理解情绪.
- 建立一个标准化的阿姆哈拉语情绪分析库对于更广泛的应用和未来的研究至关重要.
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