| 融合立场检测和主题挖掘的突发公共事件网络舆情演化研究 |
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| 引用本文： | 刘高勇,黄靖钊,艾丹祥.融合立场检测和主题挖掘的突发公共事件网络舆情演化研究[J].广东工业大学学报,2022,39(3):32-40,48. |
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| 作者姓名： | 刘高勇 黄靖钊 艾丹祥 |
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| 作者单位： | 广东工业大学 管理学院, 广东 广州 510520 |
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| 基金项目： | 广东省哲学社会科学十三五规划项目;广州市哲学社会科学发展十三五规划项目 |
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| 摘 要： | 基于立场检测和主题挖掘的突发公共事件舆情演化研究，能够帮助政府及利益相关者快速地掌握突发公共事件网络舆情的演化规律，具有重要的意义。划分具体突发公共事件的舆情生命周期，提出新的立场检测模型和主题句挖掘方法，针对每个生命周期阶段，在识别大众网民的立场信息的基础上筛选出高效用的舆情信息，再挖掘高效用舆情信息的主题，以深入分析突发公共事件主题信息的演化规律。以“杭州女子失踪案”的微博数据为例，首先将本文方法与多种方法的实验结果进行对比分析，验证了方法的有效性；然后基于实验结果进行舆情演化分析，证明了其能够在实际的突发公共事件舆情中快速聚焦关键点，较好地分析突发公共事件舆情演化规律和特点。该方法能较有效、准确地识别和分析舆情内容，为网络舆情演化的研究提供了新视角。
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| 关 键 词： | 立场检测 主题挖掘 突发公共事件 舆情演化 |
| 收稿时间： | 2021-12-22 |
A Research on Online Public Opinion Evolution of Public Emergencies Based on Stance Detection and Topic Mining |
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| Liu Gao-yong,Huang Jing-zhao,Ai Dan-xiang.A Research on Online Public Opinion Evolution of Public Emergencies Based on Stance Detection and Topic Mining[J].Journal of Guangdong University of Technology,2022,39(3):32-40,48. |
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| Authors: | Liu Gao-yong Huang Jing-zhao Ai Dan-xiang |
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| Affiliation: | School of Management, Guangdong University of Technology, Guangzhou 510520, China |
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| Abstract: | The research on the evolution of public opinions in public emergencies based on stance detection and topic mining can help the government and stakeholders quickly grasp the evolution law of online public opinions in public emergencies, which is of great significance. The public opinion life cycle of specific public emergencies is divided, and a new stance detection model and a topic sentence mining method are proposed. For each life cycle stage, on the basis of identifying the stance information of mass Internet users, the efficient public opinion information is screened out, and then the topics of efficient public opinion information are mined, so as to deeply analyze the evolution law of the topic information of public emergencies. Taking the microblog data of "A Hangzhou Woman Missing Case" as an example, firstly, the experimental results of this method and various methods are compared to verify the effectiveness of this method.Then, the evolution of public opinions is analyzed based on the experimental results of this method, which proves that this method can quickly focus on the key points in the actual public opinions of public emergencies, and better analyze the evolution law and characteristics of public opinions of public emergencies. This method can effectively and accurately identify and analyze the content of public opinions, and provide a new perspective for the study of the evolution of network public opinions. |
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| Keywords: | stance detection topic mining public emergency evolution of public opinion |
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