Improved Algorithm Study on Topic Detection and Tracking
Xiao Hon · Computer Technology and Development · 2014
The topic detection can detect hot Internet public opinion and emergencies,and can carry out the continuous tracking of the topic,which can get a real-time grasp of public opinion trends. Propose an improved algorithm for detecting and tracking based on topic clustering in this paper. First,to improve the feature vectors of document,increase the backbone vectors based on sentence trunk. Then two center vectors are extracted from each detected topic,in which one is the basic center vector and another is the main center vector. On this basis,by calculating the distance between the document vector and the corresponding center vector,the cluster analysis is performed to ensure the cohesion of each document for the same topic. Meanwhile,based on keyword extraction,the theme correlation between different topics is calculated to improve the accuracy of topic detection and tracking. Taking the top 10 sites 5 channel data for more than two weeks as the test data,the experimental results show that this method improves the accuracy of topic detection and tracking to some extent,and has certain adaptability and generalization.