Abstract
This article presents the formal definition and description of popular topics on the Internet, analyzes the relationship between popular words and topics, and finally introduces a method that uses statistics and correlation of the popular words in traffic content and network flow characteristics as input for extracting popular topics on the Internet. Based on this, this article adapts a clustering algorithm to extract popular topics and gives formalized results. The test results show that this method has an accuracy of 16.7% in extracting popular topics on the Internet. Compared with web mining and topic detection and tracking (TDT), it can provide a more suitable data source for effective recovery of Internet public opinions.
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