【摘要】 高校网络舆情事件与一般网络舆情事件具有一定的差异，为了更好的对高校网络舆情事件进行处理，笔者通过python编写爬虫程序，以“××大学”为关键词爬取××大学xb事件相关舆情数据。首先对这一事件进行生命周期阶段划分，然后对爬取的文本进行情绪分类，计算文本情绪指数，对整个舆情事件进行主题相关词汇频次分析，通过构造词汇主题强度模型对舆情不同生命周期阶段计算主题词汇讨论强度，最后通过文中的分析为高校应对网络舆情事件提供建议。更多还原

【Abstract】 There is a certain difference between the university Internet public opinion event and the general Internet public opinion event.In order to better deal with the university Internet public opinion event,the author compiles the crawler program through python,crawls the public opinion data related to the XB event of×× university with the keyword " ×× University".This paper first divides the life cycle of this event,and then classifies the sentiment of the crawling text,calculates the text sentiment index,analyzes the frequency of topic related words of the whole public opinion event,calculates the topic vocabulary discussion intensity at different stages of the public opinion life cycle by constructing the vocabulary topic intensity model,and finally through the analysis of this paper,it provides the university with the response to the network public opinion Event provides advice.更多还原