|
|
Analyzing Public Opinion on Three-Child-Policy with Sentiment Classification and Keyword Extraction |
Meng Fansi1,Zhong Han1(),Shi Shuicai2,Xie Zekun1 |
1School of Information and Cyber Security, People’s Public Security University of China, Beijing 100038, China 2TRS Information Technology Co., Ltd., Beijing 100101, China |
|
|
Abstract [Objective] This paper studies the public opinion on the three-child-policy in different Chinese provinces. [Context] Existing research on this issue addresses public opinion from the Web as a whole, and ignores the demands or concerns from individual province. These studies’ research methods are rather simple with single data source. [Methods] Firstly, we analyzed the public opinion on three-child-policy with time series method from the statistical perspective. Then, we examined their sentiments with the SVM model, and extracted keywords from the negative opinion with the CRF model. Third, we created word clouds for these keywords. Finally, we conducted research on these public opinion in different provinces and generated word clouds for them. We also examined the ties between political or economic statistics and the negative key words from different provinces. [Results] The three-child-policy was more popular than other policies during the same period. The public opinion was dominated by neutral sentiments (60.56%), followed by the positive (35.15%) and the negative ones (4.29%). Public concerns in different provinces were different and correlated to the political, economic and ecological factors. [Conclusions] Different provinces should adopt customized public opinion guidance to support the three-child-policy, which will address people’s concerns more effectively.
|
Received: 23 January 2022
Published: 16 November 2022
|
|
Fund:National Social Science Fund of China(20AZD114);Soft Science Theory Research Program of the Ministry of Public Security(2021LL39) |
Corresponding Authors:
Zhong Han
E-mail: zhonghan@ppsuc.edu.cn
|
[1] |
第六次人口普查委员会. 2010年第六次全国人口普查主要数据公报(第一号)[R]. 北京: 第六次人口普查委员会, 2010.
|
[1] |
(The Sixth Census Commission. Major Figures of the 2010 National Population Census(No. 1)[R]. Beijing: The Sixth Census Commission, 2010.)
|
[2] |
风笑天. 三孩生育政策与新型生育文化建设[J]. 新疆师范大学学报: 哲学社会科学版, 2022, 43(1): 98-105.
|
[2] |
(Feng Xiaotian. The Three-Child Policy and the Construction of the New Fertility Culture[J]. Journal of Xinjiang Normal University: Edition of Philosophy and Social Sciences, 2022, 43(1): 98-105.)
|
[3] |
中共中央国务院. 关于优化生育政策促进人口长期均衡发展的决定[R]. 北京: 中共中央国务院, 2021.
|
[3] |
(CPC Central Committee and State Council. Decision on Optimizing Birth Policy to Promote Long-Term Balanced Development of Population[R]. Beijing: CPC Central Committee and State Council, 2021.)
|
[4] |
刘毅. 网络舆情研究概论[M]. 天津: 天津人民出版社, 2007: 51.
|
[4] |
Liu Yi. Introduction to Network Public Opinion Research[M]. Tianjin: Tianjin Renmin Publishing House, 2007: 51.)
|
[5] |
李小波. 涉警舆情结构及其演化机理分析[J]. 公安学研究, 2020, 3(6): 46-66.
|
[5] |
(Li Xiaobo. The Structure and Evolution Mechanism of Police-Related Public Opinion[J]. Journal of Public Security Science, 2020, 3(6): 46-66.)
|
[6] |
王来华, 冯希莹. 舆情概念认识中的两个基本问题[J]. 天津社会科学, 2012, 3(6): 73-76.
|
[6] |
(Wang Laihua, Feng Xiying. Two Basic Problems in Public Opinion Concept Understanding[J]. Tianjin Social Sciences, 2012, 3(6): 73-76.)
|
[7] |
孙倬, 赵红, 王宗水. 网络舆情研究进展及其主题关联关系路径分析[J]. 图书情报工作, 2021, 65(7): 143-154.
doi: 10.13266/j.issn.0252-3116.2021.07.014
|
[7] |
(Sun Zhuo, Zhao Hong, Wang Zongshui. Analysis on the Association and Evolution Path of Internet Public Opinion[J]. Library and Information Service, 2021, 65(7): 143-154.)
doi: 10.13266/j.issn.0252-3116.2021.07.014
|
[8] |
叶金珠, 佘廉. 网络突发事件蔓延机理研究[J]. 情报杂志, 2012, 31(3): 1-5.
|
[8] |
(Ye Jinzhu, She Lian. The Mechanism of Internet Emergency Spread[J]. Journal of Intelligence, 2012, 31(3): 1-5.)
|
[9] |
黄微, 卢国强, 赵旭. 基于知识图谱的微博主题演变路径研究[J]. 情报理论与实践, 2022, 45(3): 173-181.
|
[9] |
(Huang Wei, Lu Guoqiang, Zhao Xu. Research on the Evolution Path of Microblog Topic Based on Knowledge Graph[J]. Information Studies: Theory & Application, 2022, 45(3): 173-181.)
|
[10] |
赵艺, 李平. 突发疫情环境下网络舆情传播趋势预测及社会保障应急机制研究[J]. 情报科学, 2021, 39(11): 45-50.
|
[10] |
(Zhao Yi, Li Ping. The Trend Forecast of Internet Public Opinion Dissemination and Social Security Emergency Mechanism in the Emergent Epidemic[J]. Information Science, 2021, 39(11): 45-50.)
|
[11] |
林玲, 陈福集, 谢加良, 等. 基于改进灰狼优化支持向量回归的网络舆情预测[J]. 系统工程理论与实践, 2022, 42(2): 487-498.
doi: 10.12011/SETP2020-1500
|
[11] |
(Lin Ling, Chen Fuji, Xie Jialiang, et al. Prediction of Network Public Opinion Based on Improved Grey Wolf Optimized Support Vector Machine Regression[J]. Systems Engineering-Theory & Practice, 2022, 42(2): 487-498.)
doi: 10.12011/SETP2020-1500
|
[12] |
金城, 吴文渊, 陈柏儒, 等. 面向不同用户群体的社交媒体台风舆情演化分析及对比研究[J]. 地球信息科学学报, 2021, 23(12): 2174-2186.
doi: 10.12082/dqxxkx.2021.210065
|
[12] |
(Jin Cheng, Wu Wenyuan, Chen Bairu, et al. Analysis and Comparative Study of the Evolution of Public Opinion on Social Media During Typhoon for Different User Groups[J]. Journal of Geo-Information Science, 2021, 23(12): 2174-2186.)
doi: 10.12082/dqxxkx.2021.210065
|
[13] |
韩鹏宇, 余正涛, 高盛祥, 等. 案件要素句子关联图卷积的案件舆情摘要方法[J]. 软件学报, 2021, 32(12): 3829-3838.
|
[13] |
(Han Pengyu, Yu Zhengtao, Gao Shengxiang, et al. Case-Related Public Opinion Summarization Method Based on Graph Convolution of Sentence Association Graph with Case Elements[J]. Journal of Software, 2021, 32(12): 3829-3838.)
|
[14] |
江志英, 李宇洋, 李佳桐, 等. 基于层次分析的长短记忆网络(AHP-LSTM)的食品安全网络舆情预警模型[J]. 北京化工大学学报(自然科学版), 2021, 48(6): 98-107.
|
[14] |
(Jiang Zhiying, Li Yuyang, Li Jiatong, et al. An Early-Warning Model Based on an Analytic Hierarchy Process-Long Short-Term Memory Network(AHP-LSTM) for Food Safety Network Public Opinion[J]. Journal of Beijing University of Chemical Technology (Natural Science Edition), 2021, 48(6): 98-107.)
|
[15] |
风笑天. “二孩”还是“三孩”, “允许”还是“提倡”?——国家生育政策调整的目标解读与认识转变[J]. 江苏行政学院学报, 2021(5): 51-59.
|
[15] |
(Feng Xiaotian. “Two Children” or “Three Children, “Allowing” or “Advocating”: Interpretation of the Policy Objectives of the National Family Planning Policy and Awareness Shift[J]. The Journal of Jiangsu Administration Institute, 2021(5): 51-59.)
|
[16] |
风笑天. 三孩生育意愿预测须防范二孩研究偏差[J]. 探索与争鸣, 2021(11): 80-89.
|
[16] |
(Feng Xiaotian. The Prediction of Three-Child Fertility Intention Must Avoid the Deviation of Second-Child Research[J]. Exploration and Free Views, 2021(11): 80-89.)
|
[17] |
杨燕绥, 于淼. 三孩政策目标与护养融合体系建设[J]. 行政管理改革, 2021(9): 26-34.
|
[17] |
(Yang Yansui, Yu Miao. The Goal of Three-Child Policy and Medical Care and Nursing Integration System Construction[J]. Administration Reform, 2021(9): 26-34.)
|
[18] |
陈卫. 中国的低生育率与三孩政策——基于第七次全国人口普查数据的分析[J]. 人口与经济, 2021(5): 25-35.
|
[18] |
(Chen Wei. China’s Low Fertility and the Three-Child Policy: Analysis Based on the Data of the Seventh National Census[J]. Population & Economics, 2021(5): 25-35.)
|
[19] |
李丹, 李丽萍, 李丹. 三孩政策出台的舆情效应及启示——基于NLP的网络大数据分析[J]. 中国青年研究, 2021(10): 46-53.
|
[19] |
(Li Dan, Li Liping, Li Dan. The Public Opinion Effect and Enlightenment of the Three-Child Policy Network Big Data Analysis Based on NLP[J]. China Youth Study, 2021(10): 46-53.)
|
[20] |
Vapnik V, Levin E, Cun Y L. Measuring the VC-Dimension of a Learning Machine[J]. Neural Computation, 1994, 6(5): 851-876.
doi: 10.1162/neco.1994.6.5.851
|
[21] |
胡少虎, 张颖怡, 章成志. 关键词提取研究综述[J]. 数据分析与知识发现, 2021, 5(3): 45-59.
|
[21] |
(Hu Shaohu, Zhang Yingyi, Zhang Chengzhi. Review of Keyword Extraction Studies[J]. Data Analysis and Knowledge Discovery, 2021, 5(3): 45-59.)
|
[22] |
韩运荣, 明山, 何睿敏. 生育政策调整背景下的“女性与生育”微博舆情研究[J]. 中国新闻传播研究, 2020(1): 124-141.
|
[22] |
(Han Yunrong, Ming Shan, He Ruimin. Public Sentiment Analysis of “Women and Birth” on Weibo Against the Backdrop of Policy Change[J]. China Journalism and Communication Journal, 2020(1): 124-141.)
|
[23] |
第七次人口普查委员会. 2021年第七次全国人口普查主要数据公报(第一号)[R]. 北京: 第七次人口普查委员会, 2020.
|
[23] |
(The Seventh Census Commission. Major Figures of the 2021 National Population Census(No. 1)[R]. Beijing: The Seventh Census Commission, 2020.)
|
[24] |
中国国家统计局. 中国统计年鉴[R]. 北京: 国家统计局, 2020.
|
[24] |
(National Bureau of Statistics of China. China Statistical Yearbook[R]. Beijing: National Bureau of Statistics, 2020.)
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|