Building multi-subtopic Bi-level network for micro-blog hot topic based on feature Co-Occurrence and semantic community division

摘要

The multi-subtopic is challenging to be understood timely and comprehensively due to micro-blog characteristics, such as low-value density, and fast update speed. For such an issue, this paper proposes a Multi-Subtopic Bi-level Network (MSBN) for micro-blog hot topics based on feature co-occurrence and semantic community division to support users understanding better the subject. First, the highlighted words are extracted by combining two coefficients including the micro-blog importance (e.g., the number of comments and the number of praises) and the time decay. The compound co-occurrence rates (i.e., global and local co-occurrence rates) are used to measure the correlation strength between any two highlighted words, while the global semantic of a micro-blog hot topic can be shown as a complex network whose nodes are the extracted feature words and edges are relations between any two feature words. Next, an improved weighted modularity function is proposed as a criterion for the community division. The complex network of a topic is divided into some semantic communities, where each is regarded as a subtopic of the given micro-blog topic. Subsequently, the genetic algorithm is used to calculate the maximum of weighted modularity and achieve community division of complex networks, so finally, the terminal location of each micro-blog in a different semantic community is obtained to draw regional location map and analyze the supporting propensity of each region to the micro-blog hot topic. Experimental results show that the proposed model can accurately and effectively represent the multi-subtopic of a micro-blog hot topic in the current time that supports users to discover and understand the micro-blog hot topic, allowing users to identify and understand the concerned differences among different regions for the same micro-blog hot topic.