@inproceedings{jingjian-etal-2024-ji,
title = "基于主题模型与图神经网络的突发公共卫生事件国际舆情演化分析研(International Public Opinion Evolution Analysis on Sudden Public Health Events using Topic Model and Graph Neural Network)",
author = "Jingjian, Gao and
Guoming, Sang and
Zhi, Liu and
Yijia, Zhang and
Hongfei, Lin",
editor = "Sun, Maosong and
Liang, Jiye and
Han, Xianpei and
Liu, Zhiyuan and
He, Yulan",
booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-1.39/",
pages = "504--514",
language = "zho",
abstract = "{\textquotedblleft}研究突发公共卫生事件国际舆情演变规律,对国际舆情资源进行应急管理和舆论疏导有重要借鉴价值。本文使用谷歌新闻数据库以各国针对COVID-19的报道为对象,构建国际舆情数据集。采用主题模型、图神经网络模型,结合时间、空间维度与舆情生命周期探究全球舆论主题-情感的演化态势,模型准确率为0.7973,F1值为0.7826,性能优于其他基线模型。研究发现,各国舆情呈现放射传播状态。国际媒体舆论的情感倾向和讨论主题存在正相关且随时间进行转变。{\textquotedblright}"
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<abstract>“研究突发公共卫生事件国际舆情演变规律,对国际舆情资源进行应急管理和舆论疏导有重要借鉴价值。本文使用谷歌新闻数据库以各国针对COVID-19的报道为对象,构建国际舆情数据集。采用主题模型、图神经网络模型,结合时间、空间维度与舆情生命周期探究全球舆论主题-情感的演化态势,模型准确率为0.7973,F1值为0.7826,性能优于其他基线模型。研究发现,各国舆情呈现放射传播状态。国际媒体舆论的情感倾向和讨论主题存在正相关且随时间进行转变。”</abstract>
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%0 Conference Proceedings
%T 基于主题模型与图神经网络的突发公共卫生事件国际舆情演化分析研(International Public Opinion Evolution Analysis on Sudden Public Health Events using Topic Model and Graph Neural Network)
%A Jingjian, Gao
%A Guoming, Sang
%A Zhi, Liu
%A Yijia, Zhang
%A Hongfei, Lin
%Y Sun, Maosong
%Y Liang, Jiye
%Y Han, Xianpei
%Y Liu, Zhiyuan
%Y He, Yulan
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G zho
%F jingjian-etal-2024-ji
%X “研究突发公共卫生事件国际舆情演变规律,对国际舆情资源进行应急管理和舆论疏导有重要借鉴价值。本文使用谷歌新闻数据库以各国针对COVID-19的报道为对象,构建国际舆情数据集。采用主题模型、图神经网络模型,结合时间、空间维度与舆情生命周期探究全球舆论主题-情感的演化态势,模型准确率为0.7973,F1值为0.7826,性能优于其他基线模型。研究发现,各国舆情呈现放射传播状态。国际媒体舆论的情感倾向和讨论主题存在正相关且随时间进行转变。”
%U https://aclanthology.org/2024.ccl-1.39/
%P 504-514
Markdown (Informal)
[基于主题模型与图神经网络的突发公共卫生事件国际舆情演化分析研(International Public Opinion Evolution Analysis on Sudden Public Health Events using Topic Model and Graph Neural Network)](https://aclanthology.org/2024.ccl-1.39/) (Jingjian et al., CCL 2024)
ACL