使用深度学习和词嵌入来预测人类的同意行为
Raed Alsini1, Anam Naz2, Hikmat Ullah Khan3
1Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Scientific reports
|December 2, 2024
概括
深度学习模型现在可以使用社交媒体文本来预测一种人格特征 - - 愉悦度. 使用Bi-LSTM嵌入句子的准确率达到了91.57%,优于其他方法.
科学领域:
- 人工智能的人工智能
- 计算语言学 计算语言学
- 心理学 心理学 心理学
背景情况:
- 深度学习的进步彻底改变了自然语言处理 (NLP),使机器能够解释和生成人类语言.
- 社交媒体的普及产生了大量的文本数据,为分析人类行为和个性提供了新的途径.
- 了解人格特征对于招聘,职业咨询和营销方面的应用至关重要.
研究的目的:
- 使用深度学习模型预测可接受性人格特征.
- 要区分那些更有情感的人与那些更有逻辑和理性的个人.
- 分析与可接受性相关的行为,如合作,友好和尊重不同观点.
主要方法:
- 应用浅层机器学习,集合模型和最先进的基于变压器的深度学习模型.
- 探索包括TF-IDF和POS标签在内的文本特征.
- 使用词嵌入 (word2vec,GloVe) 和句子嵌入用于特征工程.
主要成果:
- 使用Bi-LSTM算法嵌入句子实现了91.57%的最高性能.
- 这种方法表明,与文献中的现有方法相比,它具有更高的预测能力.
- 分析证实了深度学习在从文本中解码复杂的人类个性特征方面的有效性.
结论:
- 深度学习,特别是句子嵌入和Bi-LSTM,提供了一个强大的工具来预测人格特征,如可取性.
- 这项研究强调了NLP和AI在从社交媒体数据中理解人类行为的潜力.
- 准确的个性预测对包括人力资源和消费者行为分析在内的各种领域具有重大意义.
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