Research on emotion monitoring of public based on social network big data

Aili LI; Zishuai ZHANG; Yin LIN; Qiuju WANG; Jianan YANG; Weicheng MENG; Yanfeng ZHANG

doi:10.11959/j.issn.2096-0271.2022054

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Research on emotion monitoring of public based on social network big data

STUDY | 更新时间：2024-06-03

- Research on emotion monitoring of public based on social network big data
- Big data research Vol. 8, Issue 6, Pages: 105-126(2022)
- 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：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2022054
  CLC： TP311.13
- Online First：2022-11，
  
  Published：15 November 2022
- 稿件说明：
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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：

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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School of Computer Science ＆Technology, Beijing Institute of Technology

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