基于社交网络大数据的民众情感监测研究

李爱黎; 张子帅; 林荫; 王秋菊; 杨建安; 孟炜程; 张岩峰

doi:10.11959/j.issn.2096-0271.2022054

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基于社交网络大数据的民众情感监测研究

研究 | 更新时间：2024-06-03

- 基于社交网络大数据的民众情感监测研究
- Research on emotion monitoring of public based on social network big data
- 大数据 2022年8卷第6期页码：105-126
- 作者机构：
  
  1. 东北大学计算机科学与工程学院，辽宁沈阳 110000
  2. 东北大学外国语学院，辽宁沈阳 110000
- 作者简介：
  
  [ "李爱黎（1995- ），女，东北大学计算机科学与工程学院硕士生，主要研究方向为情感分析、数据挖掘" ]
  [ "张子帅（2000- ），男，东北大学计算机科学与工程学院本科生，主要研究方向为数据挖掘、机器学习" ]
  [ "林荫（1984- ），女，东北大学外国语学院讲师，主要研究方向为中日文化比较研究" ]
  [ "王秋菊（1962- ），女，东北大学外国语学院教授，主要研究方向为中日文化比较研究、科技与文化研究" ]
  [ "杨建安（2002- ），男，东北大学计算机科学与工程学院本科生，主要研究方向为数据挖掘、机器学习" ]
  [ "孟炜程（2002- ），男，东北大学计算机科学与工程学院本科生，主要研究方向为数据挖掘、机器学习" ]
  [ "张岩峰（1982- ），男，东北大学计算机科学与工程学院教授，中国计算机学会高级会员，主要研究方向为大数据挖掘、大规模机器学习、分布式系统" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(62072082);辽宁省重点研发计划;The National Key Research and Development Program of Liaoning Province(2020JH2/10100037);中央高校基本科研业务费;Fundamental Research Funds for the Central Universities(2216015)
- DOI：10.11959/j.issn.2096-0271.2022054
  中图分类号： TP311.13
- 网络首发：2022-11，
  
  纸质出版：2022-11-15
- 稿件说明：
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李爱黎, 张子帅, 林荫, 等. 基于社交网络大数据的民众情感监测研究[J]. 大数据, 2022,8(6):105-126.

Aili LI, Zishuai ZHANG, Yin LIN, et al. Research on emotion monitoring of public based on social network big data[J]. Big data research, 2022, 8(6): 105-126.
李爱黎, 张子帅, 林荫, 等. 基于社交网络大数据的民众情感监测研究[J]. 大数据, 2022,8(6):105-126. DOI： 10.11959/j.issn.2096-0271.2022054.

Aili LI, Zishuai ZHANG, Yin LIN, et al. Research on emotion monitoring of public based on social network big data[J]. Big data research, 2022, 8(6): 105-126. DOI： 10.11959/j.issn.2096-0271.2022054.

摘要

近年来，新浪微博、推特等社交网络平台逐渐成为反映社会舆情的主要载体之一，为网民发表观点和表达情绪提供了便利。基于社交网络大数据的舆情监控已经成为新的研究热点，利用各国的社交网络大数据进行民众情感监测，有助于直接掌握国际关系中的民众情感倾向，对我国外交、对外贸易等方面都有很重要的作用。基于此，提出了一种面向中日语料的民众情感监测系统，该系统能够同时分析新浪微博和推特等社交平台的中日文语料数据中包含的情感倾向，并以可视化的形式展现给用户。情感分析算法方面，在BERT模型基础上结合自扩展的中日文情感词典，提出了一个新的情感分析模型——EmoBERT。实验结果表明，相比于原始BERT模型，EmoBERT模型在中文情感分类任务和日文情感分类任务上都取得了很好的表现。其中中文模型EmoBERT-C将中文BERT模型准确率从89.68%提升至92.15%，日文模型EmoBERT-J将日文BERT模型准确率从74.73%提升至78.26%。

Abstract

In recent years

social networking platforms such as Sina Weibo and Twitter have gradually become one of the main carriers for reflecting social public opinion

providing a convenient platform for netizens to express their opinions and emotions.Public opinion monitoring based on social network big data has become a new research hotspot.People’s emotions monitoring using social network big data in various countries is helpful to directly grasp people’s emotional tendencies in international relations

and has a great impact on the diplomacy

foreign trade

and other aspects.Based on this

a public sentiment monitoring system for Chinese and Japanese data was proposed

which could analyze the emotional tendencies contained in Chinese and Japanese data on social platforms such as Sina Weibo and Twitter simultaneously

and displayed them to users in a visual form.In the aspect of sentiment analysis algorithm

based on the BERT model and combined with the self-expanding Chinese and Japanese sentiment lexicon

a new sentiment analysis model

EmoBERT

was proposed.The experimental results show that

compared with the original BERT model

the EmoBERT has achieved good results on both Chinese sentiment classification tasks and Japanese sentiment classification tasks.Among them

EmoBERT-C increases the accuracy of Chinese BERT from 89.68% to 92.15%

and EmoBERT-J increases the accuracy of Japanese BERT model from 74.73% to 78.26%.

关键词

Keywords

references

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