| 大数据驱动的社交网络舆情生态性评价及实证研究 |
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| 引用本文： | 王铎,王晰巍,贾若男,郑晴晓.大数据驱动的社交网络舆情生态性评价及实证研究[J].情报资料工作,2020,41(2):56-63. |
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| 作者姓名： | 王铎 王晰巍 贾若男 郑晴晓 |
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| 作者单位： | 吉林大学管理学院 长春130022;吉林大学管理学院 长春130022;吉林大学大数据管理研究中心 长春 130022;吉林大学国家发展与安全研究院 长春130022;中译语通科技股份有限公司 北京100131 |
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| 基金项目： | 国家社科基金重大项目“大数据驱动的社交网络舆情主题图谱构建及调控策略研究”(项目编号:18ZDA310)的研究成果。 |
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| 摘 要： | 目的/意义]在大数据时代,基于客观数据构建行之有效的社交网络舆情生态评价方法对网络生态治理和健康发展具有重要的意义。方法/过程]本文以信息生态理论为基础,采用机器学习、敏感判断、关键词抽取等自然语言处理技术构建了社交网络舆情生态性评价算法。在数据处理过程中,采用基于Adaboost的集成学习方法,利用差异方法、特征集合构造分类器之间的互补效应,通过有效聚合多个基于统计和基于规则的情绪分析器,构建出情感分析模型,为评价指标体系提供支撑。实践层面,本文选出东北、沿海以及西部几个代表性区域运用所构建的评价算法对区域生态性进行评价和分析。结果/结论]该评价方法的构建为政府、网站、网民携手净化社交网络空间具有重要的指导意义,并为社交网络舆情主题图谱的构建及调控策略的研究提供了重要的理论和实践基础。
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| 关 键 词： | 数据采集 机器学习 社交网络舆情 生态性评价 |
Ecological Evaluation and Empirical Study of Public Opinion in Social Network Driven by Big Data |
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| Wang Duo,Wang Xiwei,Jia Ruonan,Zheng Qingxiao.Ecological Evaluation and Empirical Study of Public Opinion in Social Network Driven by Big Data[J].Information and Documentation Services,2020,41(2):56-63. |
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| Authors: | Wang Duo Wang Xiwei Jia Ruonan Zheng Qingxiao |
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| Institution: | (School of Management,Jilin University,Changchun,130022;Research Center for Big Data Management,Jilin University,Changchun,130022;Institute of National Development and Security,Jilin University,Changchun,130022;Global Tone Communication Technology,Beijing,100131) |
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| Abstract: | Purpose/significance]In big data age,it is of great significance to construct an effective public opinion evaluation method based on the data from social media,and such method could contribute to network ecological governance and a healthy network development.Method/process]Based on the information ecology theory,this paper constructs a social-network-based public opinion evaluation method using natural language processing techniques such as machine learning,sensitive judgment and keyword extraction.In the process of data processing,multiple methods were used to detect public sentiment so as to provide supporting evidence for evaluating the proposed indicator system.Such as,adaptive learning based on Adaboost,creating classifiers by using difference method and feature set,as well as statistical-based and rule-based sentiment analysis.On the practical level,the current paper selects several representative regions in the northeast,coastal,and western regions of China to evaluate regional ecological characteristics under the proposed evaluating method.Result/conclusion]The construction of this evaluation method could provide guiding for government,websites and netizens to work together and develop the social network space.It could also provide both theoretical and practical basis for future research in social network knowledge graph construction and network regulation strategy studies. |
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| Keywords: | data mining machine learning social network public opinion ecological evaluation |
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