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在西班牙语文本集上应用数据增强方法的指南,用于情感和情感分析

Rodrigo Gutiérrez Benítez¹, Alejandra Segura Navarrete¹, Christian Vidal-Castro¹

¹Information Systems Department, Universidad del Bio-Bío, Concepción, Bio-Bío, Chile.

|September 26, 2024

概括

数据增强技术显著改善了西班牙语文本的情感和情感分析. 机器学习和深度学习模型显示了增强的性能,特别是卷积神经网络 (CNN) 和来自变压器的双向编码器表示 (BETO).

科学领域:

自然语言处理 (NLP) 是一种自然语言处理.
机器学习 (ML) 是指机器学习.
深度学习 (DL) 是指深度学习.
计算语言学计算语言学

背景情况:

社交媒体是意见和情绪的丰富数据来源,需要先进的分析技术.
情感分析 (EA) 和情感分析 (SA) 是理解文本的关键NLP任务.
有限的标记西班牙数据集阻碍了EA和SA的ML/DL模型的有效性.

研究的目的:

研究数据增强 (DA) 技术在增强西班牙语文本情感和情感分析方面的有效性.
评估各种DA方法对ML和DL分类性能的影响.
为西班牙NLP任务选择合适的算法和增强策略提供指导.

主要方法:

应用文本操纵技术,包括转换,转述 (逆向翻译) 和生成对抗网络 (GAN).
利用不同的西班牙语数据集:歌词,社交媒体评论,新闻头条和学生调查答复.
使用卷积神经网络 (CNN) 和来自变压器的双向编码器表示 (BETO) 模型评估性能.

主要成果:

在攻击性数据集上,CNN通过情感文本生成对抗网络 (SentiGan) 实现了18%的改进.
美国有线电视新闻网 (CNN) 显示,在基于性别的暴力数据集上,Easy Data Augmentation (EDA) 的使用率提高了11%.
在10月18日的数据集上,BETO的表现增加了10%,在教学调查数据集上,EDA的表现增加了4%.

结论:

数据增强技术明显提高了西班牙语文本的情感和情感分析性能.
增强技术和ML/DL模型的选择影响了分类结果,建议对数据集进行特定的优化.
这项研究为分析西班牙语的主观性提供了宝贵的见解,并为选择有效的NLP策略提供了信息.

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