Time-sensitive self-adaptive on-line subtopic detecting method and system

Abstract

The invention relates to a time-sensitive self-adaptive on-line subtopic detecting method and system. The method comprises the following steps: (1) vectorizing each document in a document flow; (2) carrying out incremental clustering on the documents and adjusting the central weight of subtopics according to the document weight decaying with time; (3) combining the subtopics or deleting the meaningless subtopics when the quantity of the subtopics generated by clustering or the weight ratio of a certain subtopic meets the threshold conditions or the subtopics meet the long tail detection conditions; and (4) generating a summary of the new subtopics, outputting and displaying according to the weight of each new subtopic and the internal document distribution thereof. The system comprises a document representing module, an incremental clustering module, a new subtopic discovering module and a summary generating module. According to the method and the system which are disclosed by the invention, the historical document weight decays with time and the dynamic update on the quantity and the content of the subtopics is carried out on the basis of the threshold judgment and the long tail detection, so that the subtopic detecting efficiency can be effectively increased.

Description

technical field [0001] The invention belongs to the field of information technology, and specifically relates to a time-sensitive and self-adaptive subtopic online detection method and system, which can be applied to emergency event detection, subtopic analysis, public opinion analysis, social media data mining and other fields. Background technique [0002] Weibo is short for Microblog. By registering a Weibo account, users can follow friends, celebrities, institutions, etc., so that different users can establish network relationships. The news flow of Weibo is full of various things, but different social entities pay attention to completely different content. For example, product companies pay attention to the real-time word-of-mouth of related products on the Internet, and famous people pay attention to their public opinion image among netizens. with impact. Therefore, online subtopic detection for specific target entities based on social networks has attracted great at...

AI Technical Summary

Problems solved by technology

[0009] The above system has the following problems: First, it does not distinguish between the weight of historical documents and the weight of latest documents

Second: Unable to adjust the content and quantity of subtopics adaptively

Third: The system based on burst detection can only get sudden events, but cannot detect popular events (subtopics), that is, events that the public cares about for a long time (hot subtopics) cannot be detected

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