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浙江大学学报(工学版)  2022, Vol. 56 Issue (11): 2271-2279    DOI: 10.3785/j.issn.1008-973X.2023.02.018
计算机技术     
不同类型重大公共事件下交通管控舆情分析
汤文蕴(),丁子羿,马健霄
南京林业大学 汽车与交通工程学院,江苏 南京 210037
Public opinion analysis on traffic control under different major public events
Wen-yun TANG(),Zi-yi DING,Jian-xiao MA
College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
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摘要:

为了研究在不同类型重大公共事件下实施交通管控引起的舆情差异,从网络舆情角度,构建适用于交通管控舆情分析的情感与主题挖掘协同分析模型. 在模型中建立面向重大公共事件下交通管控舆情的情感词典库,基于朴素贝叶斯分类算法进行情感分析,采用LDA主题模型法进行主题挖掘. 以常规公共事件与突发公共事件下的交通管控为研究对象,通过爬取微博平台网民的评论数据,对比不同类型事件下交通管控网络舆情变化差异. 结果表明,常规公共事件与突发公共事件下的交通管控舆情情感值分别为0.75~0.95与0.35~0.85. 不同类型重大公共事件下交通管控舆情出现消极主题词的因素更加复杂,而积极主题词的相似度更高. 突发公共事件中交通管控舆情主题词的热度变化趋势在事件初期急剧上升,然后逐渐下降趋于平缓,而常规公共事件下的趋势没有明显规律.

关键词: 公共事件交通管控舆情分析情感分析主题挖掘    
Abstract:

A collaborative model of sentiment analysis and topic mining suitable for public opinion analysis on traffic control from perspective network public opinion was constructed to study the differences in public opinion on traffic control under different types of major public events. In the model, a sentiment dictionary library for public opinion on traffic control under major public events was established and sentiment analysis was performed based on Naive Bayesian classification algorithm. The latent dirichlet allocation (LDA) topic model was employed for topic mining. Taking traffic control under regular public events and emergency public events as the research object, the differences in the changes of network public opinion on traffic control under different types of events was compared by crawling comment data of netizens on the Weibo platform. The public opinion sentiment values of traffic control under regular public events and emergency public events are 0.75~0.95 and 0.35~0.85 respectively. The factors for occurrence of negative topic words of public opinion under different types of major public events are more complex, while the similarity of positive topic words under two types of events is high. Under public emergencies, the popular trend of topic words rise sharply in the early stage and then gradually declined to be flat. The trend of the popularity under regular public events has no obvious regularity.

Key words: public events    traffic control    public opinion analysis    sentiment analysis    topic mining
收稿日期: 2021-11-30 出版日期: 2022-12-02
CLC:  U 11  
基金资助: 江苏省高等学校自然科学研究面上项目(20KJB580016);江苏高校哲学社会科学研究一般项目(2020SJA0133);教育部产学合作协同育人资助项目(202102055014)
作者简介: 汤文蕴(1988—),男,讲师,从事交通管理研究. orcid.org/0000-0002-2552-7963. E-mail: tangwy@njfu.edu.cn
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引用本文:

汤文蕴,丁子羿,马健霄. 不同类型重大公共事件下交通管控舆情分析[J]. 浙江大学学报(工学版), 2022, 56(11): 2271-2279.

Wen-yun TANG,Zi-yi DING,Jian-xiao MA. Public opinion analysis on traffic control under different major public events. Journal of ZheJiang University (Engineering Science), 2022, 56(11): 2271-2279.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2023.02.018        https://www.zjujournals.com/eng/CN/Y2022/V56/I11/2271

图 1  LDA主题模型结构图
图 2  情感分析与主题挖掘协同模型
图 3  不同交通管控事件情感值分布图
词典类型 PR/% RE/% F1
网络情感词典 0.71 0.77 0.74
交通舆情词典 0.79 0.81 0.80
表 1  不同情感词典的分析准确性评估
图 4  不同交通管控事件积极与消极主题词的评论困惑度
图 5  广州疫情交通管控事件主题词强度分布
图 6  端午假期交通管控事件主题词强度分布
图 7  广州疫情交通管控事件热点主题词排序
图 8  端午假期交通管控事件热点主题词排序
图 9  不同交通管控事件热点主题词随时间变化趋势
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