在智利的关键事件期间,分析数字社会互动和Twitter (X) 中的情绪分类
Pablo A Henríquez1, Francisco Alessandri2
1Facultad de Administración y Economía, Universidad Diego Portales, Santiago, Chile.
Heliyon
|December 13, 2024
概括
社交媒体的内容在重大程度上影响了大流行和抗议等危机期间的公众反应. 先进的深度学习模型改进了情绪分析,以获得更好的危机沟通策略.
科学领域:
- 社会科学 社会科学 社会科学
- 计算机科学 计算机科学
- 沟通研究 沟通研究
背景情况:
- 社交媒体在关键事件期间塑造公众的看法和行为方面发挥着至关重要的作用.
- 分析社交媒体动态对于理解社会对危机的反应至关重要.
- 之前的情绪分析模型在准确性和效率方面存在局限性.
研究的目的:
- 探索社交媒体内容对智利关键事件期间社会态度和行动的影响.
- 引入新的指标来评估社交媒体内容的情绪,包容性,参与度和影响.
- 通过使用先进的深度学习技术,提高情绪分类准确性.
主要方法:
- 利用智利关键事件 (COVID-19,抗议活动,野火) 的新型推特数据集.
- 开发了情绪,包容性,参与度和影响分析的新指标.
- 使用深度随机向量功能链接 (D-RVFL) 神经网络进行情绪分类,与传统模型 (SVM,天真贝叶斯,BP) 进行比较.
主要成果:
- 与传统模型相比,深度随机向量功能链接 (D-RVFL) 神经网络实现了更高的情绪分类准确性 (78.30%).
- 确定了影响者,媒体,广播和电视在危机期间传播信息方面所扮演的独特角色.
- 证明了深度学习对更快,更精确的情绪分析的有效性.
结论:
- 深度学习技术,特别是D-RVFL,在紧急情况下分析社交媒体情绪方面提供了显著的改进.
- 了解各种社交媒体参与者的细微角色对于有效的危机沟通至关重要.
- 该研究为分析关键事件中的社交媒体动态提供了一个全面的框架.
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