使用深度神经网络在短文本中检测负面情绪
Luis A Camacho-Vázquez1, Vanessa A Camacho-Vázquez2, Sandra D Orantes-Jiménez1
1Instituto Politécnico Nacional - Centro de Investigación en Computación, Mexico City, Mexico.
Cyberpsychology, behavior and social networking
|May 7, 2025
概括
这项研究提高了西班牙语推特中的情绪检测,发现BERT模型在识别悲伤和愤怒等负面情绪方面非常有效. 这项研究解决了西班牙语社区在NLP中的关键差距.
科学领域:
- 计算语言学 计算语言学
- 情感计算是一种情感计算.
- 自然语言处理自然语言处理.
背景情况:
- 情绪检测在心理学,健康,社会科学和营销学中至关重要.
- 在西班牙语推特中识别负面情绪,由于背景,文化和模两可的原因,提出了独特的挑战.
- 现有的研究主要集中在英语上,忽视了西班牙语人口.
研究的目的:
- 通过分析推特中的负面情绪来弥合西班牙情绪检测的差距.
- 将传统的机器学习方法与包括BERT和RoBERTa在内的深度学习模型进行评估.
- 确定最有效的方法来分类西班牙社交媒体中的负面情绪.
主要方法:
- 利用了12000条西班牙语推文的集体,标记了Ekman的负面情绪 (悲伤,愤怒,恐惧,厌恶).
- 评估了传统特征 (n-grams,语法 n-grams) 和深度学习模型 (CNNs,BERT,RoBERTa).
- 使用诸如宏观F1得分和训练期间报告的碳排放等指标进行性能比较.
主要成果:
- 变压器的双向编码器表示 (BERT) 获得了最高的性能,宏F1得分为0.9973.
- 先进的变压器模型和特征工程显著影响了分类准确性.
- 训练不同模型的碳排放被记录下来.
结论:
- 在西班牙语推文中,BERT在检测负面情绪方面表现出卓越的表现.
- 该研究为情感分析提供了高质量的,大规模的西班牙推特群体.
- 强调了特征,参数和预处理对NLP模型性能的重大影响.
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