基于文本挖掘的微博舆情主题图谱可视化研究

doi:10.13998/j.cnki.issn1002-1248.20-1196

摘要/Abstract

摘要： [目的/意义]从海量微博舆情用户评论文本中快速挖掘用户关注内容,能够帮助舆情管控主体更高效得对微博舆情演进和发展态势进行管理。[方法/过程]本文以新浪微博为例,基于主题图谱理论和文本挖掘方法构建微博舆情用户评论主题图谱,使用CiteSpace进行可视化分析,通过应用不同文本相似度算法、网络优化算法和文本聚类算法构建二维主题图谱并分析图谱的结构特征。[结果/结论]构建的微博舆情主题图谱能够帮助舆情管理者快速准确识别用户关注内容,同时对社交媒体上用户发布文本管理,预测舆情演化趋势,防止不良舆情滋生和扩散都具有重要作用。

关键词: 微博舆情, 文本挖掘, 主题图谱, 可视化

Abstract: [Purpose/Significance] Identifying users' concerns from massive comments on Weibo can help the administrative departments to manage the evolution and development trend of public opinion. [Method/Process] Taking Weibo as an example, this paper constructed the topic graph of Weibo public opinion based on the knowledge graph theory and the text mining method. By applying different text similarity, network optimization and text clustering algorithms, the structural characteristics of the graphs were analyzed. [Results/Conclusions] The construction of the topic graph of Weibo public opinion can help managers quickly identify users' concerns. At the same time, it plays an important role in managing users' online texts, predicting the evolution trend, and preventing the diffusion of negative public opinion on social media.

Key words: Weibo public opinion, text mining, topic graph, visualization

中图分类号:

G203

邢云菲, 李玉海. 基于文本挖掘的微博舆情主题图谱可视化研究[J]. 农业图书情报学报, 2021, 33(7): 12-23.

XING Yunfei, LI Yuhai. Visualization of Topic Graph of Weibo Public Opinion Based on Text Mining[J]. Journal of Library and Information Science in Agriculture, 2021, 33(7): 12-23.

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