检索结果-南通市图书馆

Data Mining and Spatial Analysis of Social Media Text Based on the BERT-CNN Model to Achieve Situational Awareness: a Case Study of COVID-19

引用

Journal of Geodesy and Geoinformation Science 2022年第2期5卷 38-48页

作者： Jiawei ZHANG Hua QI Faculty of Geosciences and Environmental Engineering Southwest Jiaotong UniversityChengdu 611756China China Railway First Survey and Design Institute Group Co. Ltd.Xi’an 710043China

In response to the COVID-19,social media big data has played an important role in epidemic warning,tracking the source of infection,and public opinion monitoring,providing strong technical support for China’s epidemic prevention and control work.The paper used Sina Weibo posts related to COVID-19 hashtags as the data source,and built a BERT-CNN deep learning model to perform fine-grained and high-precision topic classificationon massive social media posts.Taking Shenzhen as a region of interest,we mined the“epidemic data bulletin”and“daily life impact”posts during the epidemic for spatial analysis.The results show that the confirmed communities and designated hospitals in Shenzhen as a whole present the characteristics of“sparse east and dense west”,and there is a strong positive spatial correlation between the number of confirmed cases and social media response.Specifically,Nanshan District,Futian District and Luohu District have more confirmed cases due to large population movements and dense transportation networks,and social media has responded more violently,and people’s lives have been greatly affected.However,Yantian District,Pingshan District and Dapeng New District showed opposite characteristics.The case study results further show that using deep learning methods to mine text information in social media is scientifically feasible for improving situational awareness and decision support during the COVID-19.

关键词： COVID-19 Sina Weibo BERT-CNN topic classification situational awareness

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Analysis of the trend of global power sources based on comment emotion mining

引用

Global Energy Interconnection 2020年第3期3卷 283-291页

作者： Shengxiang Zhang Chao Shi Xin Jiang Ying Zhang Lu Zhang Global Energy Interconnection Development and Cooperation Organization No.8 Xuanwumennei StreetBeijing 100031P.R.China POWERCHINA Changchun Electric Power Engineering Co. Ltd.Changchun CityJilin Province130062P.R.China Nanrui Group Co. Ltd.Chenxin StreetNanjingJiangsu Province210003P.R.China Aktins The Axis10 Holliday StreetBirminghamB11TFUnited Kingdom

In recent years,renewable energy technologies have been developed vigorously,and related supporting policies have been issued.The developmental trend of different energy sources directly affects the future developmental pattern of the energy and power industry.Energy trend research can be quantified through data statistics and model calculations;however,parameter settings and optimization are difficult,and the analysis results sometimes do not reflect objective reality.This paper proposes an energy and power information analysis method based on emotion mining.This method collects energy commentary news and literature reports from many authoritative media around the world and builds a convolutional neural network model and a text analysis model for topic classification and positive/negative emotion evaluation,which helps obtain text evaluation matrixes for all collected texts.Finally,a long-short-term memory model algorithm is employed to predict the future development prospects and market trends for various types of energy based on the analyzed emotions in different time spans.Experimental results indicate that energy trend analysis based on this method is consistent with the real scenario,has good applicability,and can provide a useful reference for the development of energy and power resources and of other industry areas as well.

关键词： Global energy and power trend topic classification Text emotion analysis CNN LSTM

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：