IPSO-LSTM混合模型用于在紧急情况下预测在线公众论趋势
Guangyu Mu1,2, Zehan Liao1, Jiaxue Li1
1School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, China.
PloS one
|October 10, 2023
概括
本研究引入了一种改进的混合模型,用于在紧急情况下预测在线论. IPSO-LSTM模型准确预测紧急传播趋势,帮助当局进行危机管理.
科学领域:
- 计算社会科学 计算社会科学
- 人工智能的人工智能
- 网络科学 网络科学
背景情况:
- 在紧急情况下在线讨论可以培养相互矛盾的意见和消极的情绪.
- 准确预测突然的网络公众论事件对于有效的危机管理至关重要.
- 现有的模型可能缺乏捕捉复杂的论动态所需的精度.
研究的目的:
- 开发一种新的混合预测模型,用于预测紧急公众论传播.
- 提高紧急公众论趋势分析的准确性和可靠性.
- 为当局提供一个工具,以便主动识别和管理潜在的论危机.
主要方法:
- 构建一个混合预测模型,集成粒子集群优化 (PSO) 和长短期记忆 (LSTM) 神经网络.
- 通过改进惯性重量和自适应变化操作来增强PSO算法,创建一个改进的PSO (IPSO) 算法.
- 使用IPSO算法优化LSTM网络参数,形成IPSO-LSTM模型,用于预测论趋势.
主要成果:
- 与BP,LSTM和PSO-LSTM等传统模型相比,IPSO-LSTM模型显示出更高的预测准确性.
- 平均而言,IPSO-LSTM模型在平均绝对百分比错误 (MAPE) 中比BP提高了74.27%,比LSTM提高了33.96%,比PSO-LSTM提高了13.59%.
- 实验结果证实了该模型在预测紧急公众论传播模式方面的有效性.
结论:
- IPSO-LSTM混合模型在紧急情况下预测在线公众论方面取得了重大进展.
- 这种预测能力可以帮助当局制定及时有效的危机管理战略.
- 该研究有助于促进更可持续和积极的在线环境,通过积极识别公众论危机.
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