基于BTM模型的教育舆情热点主题演化研究——以研究生招生考试为例

The Evolution of Educational Public Opinion Hot Spots Based on BTM Model——Taking Postgraduate Entrance Examination as An Example

【目的/意义】为把握招生考试过程中网络舆论的基本特点和发展规律,及时发现潜在舆情隐患,本研究对考研复试期间国内主流网络社交平台的相关话题讨论文本进行了主题演化研究。【方法/过程】使用Python采集数据,BTM模型对数据中的词对建模来进行主题挖掘和聚类,对各主题强度和内容随时间的演化进行分析。【结果/结论】主题强度和内容演化结果显示,公众的关注点与招生录取进程密切相关,并呈现一定的周期和规律性,能够做为网络舆情预测的依据。【创新/局限】BTM模型克服了短文本语料中的数据稀疏问题,能够有效进行主题挖掘,但同时也存在语义理解不足,需要人工辅助解读的问题,需要在后续研究中进一步改进。

【Purpose/significance】In order to grasp the basic characteristics and development law of network public opinion in the enrollment examination and find out the possible public opinion in time,the study makes an evolutionary study on the relevant topic texts of domestic mainstream network social platforms during the re examination of postgraduate entrance examination.【Method/process】Collect data with Python,model word pairs with BTM model,mine and classify topics.Analyze the strength and content evolution of each topic over time.【Result/conclusion】The results of theme intensity and content evolution show that public concerns are closely related to the process of enrollment,and show a certain cycle and regularity,which can be used as the basis for network public opinion prediction.【Innovation/limitation】BTM model solves the problem of data sparsity in the corpus of short articles and can effectively mine topics.However,it also has the problem of insufficient semantic understanding of machine learning and manual interpretation,which needs to be improved in follow-up research.