使用转换器和Bi-LSTM与句子嵌入用于预测开放性的人类个性特征
Anam Naz1, Hikmat Ullah Khan1, Tariq Alsahfi2
1Department of Information Technology, University of Sargodha, Sargodha, Punjab, Pakistan.
PeerJ. Computer science
|June 26, 2025
概括
这项研究使用自然语言处理 (NLP) 和深度学习预测开放性人格特征. DistilBERT实现了最高的准确性 (92%),超过了其他特征预测方法.
科学领域:
- 计算语言学 计算语言学
- 心理学信息学 心理学信息学
- 人工智能的人工智能
背景情况:
- 了解人格特征,特别是开放性,对于消费者行为分析,职业咨询和招聘等应用至关重要.
- 开放性包括感知 (实用,具体) 和直观 (抽象,创造性) 的性质,影响决策和观点.
- 准确的个性特征预测可以显著提高各种专业和个人决策过程.
研究的目的:
- 探索自然语言处理 (NLP) 功能来预测开放性人格特征.
- 应用最先进的深度学习算法来增强开放性特征预测.
- 为了比较传统的NLP功能与先进的深度学习嵌入的有效性.
主要方法:
- 使用迈尔斯-布里格斯类型指标 (MBTI) 数据集进行培训和评估.
- 与传统的NLP功能比较,例如TF-IDF (术语频率-反向文档频率) 和POS (语音部分) 标记与机器学习模型.
- 采用了来自句子嵌入的深度特征的深度学习模型,word2vec和GloVe (词语表示的全球向量).
- 评估基于变压器的模型,特别是DistilBERT,以评估它们在上下文语义捕获方面的表现.
主要成果:
- 使用梯度增强的TF-IDF实现了90%的准确性.
- 使用Bi-LSTM (双向长短期记忆) 嵌入句子的准确率达到90.5%.
- 迪斯蒂尔伯特模型表现出卓越的性能,达到92%的最高精度.
结论:
- 深度学习模型,特别是那些利用高级句子嵌入的模型,显示出对准确的人格特征预测的重大前景.
- 像DistilBERT这样的基于变压器的模型代表了使用NLP进行开放性特征预测的当前最先进的状态.
- 这项研究突出了NLP和深度学习的潜力,以改进心理评估,并为数据驱动的决策提供信息.
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