Research on trend prediction of time-coded LSTM based public opinion hot spots in universities

Jie YI; Tengfei CAO; Mingfeng HUANG; Xiaohan HUANG; Zizhen ZHANG

doi:10.11959/j.issn.2096-0271.2022034

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Research on trend prediction of time-coded LSTM based public opinion hot spots in universities

STUDY | 更新时间：2024-06-03

- Research on trend prediction of time-coded LSTM based public opinion hot spots in universities
- Big data research Vol. 8, Issue 5, Pages: 124-138(2022)
- 作者机构：
  
  1. 青海大学计算机技术与应用系，青海西宁 810016
  2. 云上贵州大数据产业发展有限公司，贵州贵阳 550081
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2022034
  CLC： TP391
- Online First：2022-09，
  
  Published：15 September 2022
- 稿件说明：
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Jie YI, Tengfei CAO, Mingfeng HUANG, et al. Research on trend prediction of time-coded LSTM based public opinion hot spots in universities[J]. Big data research, 2022, 8(5): 124-138.
DOI：

Jie YI, Tengfei CAO, Mingfeng HUANG, et al. Research on trend prediction of time-coded LSTM based public opinion hot spots in universities[J]. Big data research, 2022, 8(5): 124-138. DOI： 10.11959/j.issn.2096-0271.2022034.

摘要

随着互联网技术的发展，网络舆情热点信息能在短时间内迅速传播。预测舆情热点的发展趋势，有助于高校对学生思想健康状况进行分析管理，也是当下网络舆情信息研究领域的重要课题。针对微博中的舆情信息文本，构建基于时间编码长短期记忆网络（LSTM）的高校舆情热点趋势预测模型，并与支持向量机、循环神经网络两种模型的预测效果进行对比，验证了基于时间编码的LSTM算法在舆情趋势预测上的准确率。最后，利用微博中的高校实时舆情事件对构建的模型预测效果进行评估，并动态调整评估参数，实现了对评估性能的优化，预测效果得到了显著提升。

Abstract

With the development of Internet technology

network public opinion hot information can be quickly spread in a short time.Predicting the development trend of public opinion hot spots is helpful to the analysis and management of college students’ ideological health

and it is also an important issue in the field of network public opinion information research.Aiming at the public opinion information text in microblog

the hot spots trend prediction model of universities based on time-coded long short-term memory (LSTM) was constructed.Compared with the prediction effect of support vector machine

and recurrent neural network through experiments

the superiority of time-coded LSTM was verified.Finally

the prediction effect of time-coded LSTM was evaluated by using the real-time public opinion events of colleges and universities in microblogs

and the evaluation parameters were dynamically adjusted to optimize the performance of the evaluation

and the prediction effect was improved significantly.

关键词

Keywords

references

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