从心理语言学和情感的角度分析希望的语言
Muhammad Arif1, Moein Shahiki Tash1, Ainaz Jamshidi2
1Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Mexico City, Mexico.
Scientific reports
|October 9, 2024
概括
这项研究分析了使用自然语言处理 (NLP) 和机器学习的社交媒体希望演讲. 像LightGBM和CatBoost这样的优化模型有效地分类了希望类型,通过超参数调整展示了增强的性能.
科学领域:
- 计算社会科学 计算社会科学
- 心理语言学 心理语言学
- 人工智能的人工智能
背景情况:
- 希望是面对生活逆境的关键心理应对机制.
- 社交媒体平台是关于希望和性的公共话语的丰富来源.
- 分析希望语言需要复杂的计算方法来提取细微的特征.
研究的目的:
- 开发和评估一种自然语言处理 (NLP) 框架,用于分析社交媒体希望言论中的心理语言,情感和情感特征.
- 调查机器学习算法的有效性在分类不同类型的希望在线表达.
- 通过超参数调整优化机器学习模型性能.
主要方法:
- 使用了NLP工具,包括语言调查和词汇计数 (LIWC),NRC-emotion-lexicon和 vaderSentiment用于特征提取.
- 应用机器学习算法,如LightGBM和CatBoost,用于分类希望语音.
- 实现了超参数调整,以提高模型的效率和准确性.
主要成果:
- 确定了与各种类型的希望在社交媒体中相关的独特的心理语言,情感和情感特征.
- 机器学习模型,特别是LightGBM和CatBoost,在分类希望演讲方面表现出很高的表现.
- 超参数调整显著提高了分类模型的效率和有效性.
结论:
- NLP和机器学习为理解数字通信中的希望表达提供了强大的工具.
- 优化的机器学习模型为希望语音分析提供了传统和深度学习方法的竞争性替代方案.
- 在这个领域,超参数调整对于最大限度地提高机器学习模型的性能至关重要.
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