基于融合注意力机制LSTM的网络舆情趋势预测  

Trend Prediction of Internet Public Opinion Based on Fusion Attention Mechanism LSTM

作　　者：马永军[1,3] 郎威 MA Yong-jun;LANG Wei(College of Artificial Intelligence,Tianjin University of Science and Technology,Tianjin 300457,China;College of Economics and Management,Tianjin University of Science and Technology,Tianjin 300457,China;Center for Food Safety Management and Strategy Research,Tianjin University of Science and Technology,Tianjin 300222,China)

机构地区：[1]天津科技大学人工智能学院,天津300457 [2]天津科技大学经济与管理学院,天津300457 [3]天津科技大学食品安全管理与战略研究中心,天津300222

出　　处：《计算机仿真》2023年第1期493-498,共6页Computer Simulation

基　　金：天津市教委社会科学重大项目(2017JWZD19);中国轻工业“食品安全智能监测技术”重点实验室项目(KFKT2020A01)。

摘　　要：网络舆情的传播力、影响力与日俱增,且受到众多因素的影响,呈现出时序性、非线性、多维度等特征。于是分析不同因素的影响,对有效预测舆情的发展趋势具有重要的现实意义。通过引入注意力机制,确定不同因素的影响程度,进一步构建出融合注意力机制的LSTM网络舆情预测模型,选取“亚硝酸盐中毒”舆情事件进行实证分析。实验表明,构建的模型可以有效分析和预测舆情的发展趋势,提高了预测的准确度,为网络舆情趋势预测研究提供了一种新的思路和方法。The spreading power and influence of network public opinion are increasing day by day, and are affected by many factors, showing the characteristics of timing, non-linear and multi-dimension. Therefore, analyzing the influence of different factors have important practical significance for effectively predicting the development trend of public opinion. By introducing the attention mechanism to determine the influence degree of different factors, this paper further constructed the LSTM network public opinion prediction model integrating the attention mechanism and finally selected "nitrite poisoning" public opinion events for empirical analysis. The experiment shows that the model can effectively analyze and predict the development trend of public opinion, improve the accuracy of prediction, and provide a new way of thinking and method for the research of network public opinion trend prediction.

关 键 词：网络舆情 趋势预测 注意力机制 长短期记忆网络 大数据 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]