摘要
针对金融恐慌舆情隐蔽性强、爆发速度快、网络用语不规范等特点,提出基于LDA-BiLSTM模型的金融恐慌舆情分析方法。以金融行业新闻网页、论坛、微博、博客等为数据来源,首先基于词性过滤的LDA方法,挖掘数据中的金融热门主题,然后通过BiLSTM模型处理短文本语料库,并分析网民对热门主题的情感极性,甄别舆情预警信息内容。实验表明,基于LDA-BiLSTM模型预测金融恐慌舆情倾向的准确率达92.74%,可为管理者提供信息支持和舆情建议。
The financial panic public opinion analysis method based on the LDA-BiLSTM model is proposed to address the characteristics of strong concealment,fast outbreak speed,and non-standard online language of financial panic public opinion.Using financial industry news websites,forums,Weibo,blogs,etc.as data sources,the LDA method based on part of speech filtering is first used to mine financial hot top⁃ics in the data.Then,the BiLSTM model is used to process the short text corpus,and the emotional polarity of netizens towards hot topics is analyzed to identify the content of public opinion warning information.The experiment shows that the accuracy of predicting financial panic public opinion tendencies based on the LDA-BiLSTM model is 92.74%,which can provide information support and public opinion suggestions for managers.
作者
张思扬
匡芳君
ZHANG Siyang;KUANG Fangjun(School of Information Engineering,Wenzhou Business College,Wenzhou 325035,China)
出处
《软件导刊》
2023年第10期79-83,共5页
Software Guide
基金
教育部人文社会科学研究规划基金项目(20YJA790090)。
关键词
金融恐慌
潜在狄利克雷分布
双向长短期记忆网络
舆情分析与预测
financial panic
latent Dirichlet allocation
bi-directional long short-term memory
public opinion analysis and prediction
