A Topic Evolution Mining Algorithm of News Text Based on Feature Evolving
Zhao Xu · Chinese Journal of Computers · 2014
The research on the topic evolution mining can obtain the topic information accurately and completely at all topic episodes,which is able to help users understand the cause and effect as well as the correlation and difference of news topic.Thus,it has a very important role in Web News Search,Network Public Opinion Monitoring,Internet Incident Detection and Emergency Management,etc.Owning to lacking the in-depth analysis of the dynamic evolution of topic features over time in the existing work which only uses the mean generalization thought to extend topic features in the evolution process incrementally,a large number of irrelevant topic information is introduced into the current work.Meanwhile the low accuracy of the topic associated computation produced by current work leads to the deviation phenomena of the topic evolution mining.Aiming to deal with this issue,this paper first proposes a feature computation model of the topic-evolutionoriented through introducing the evolution characteristics of the topic feature,and then on thisbasis,the article conducts the forward fusion and reverse filter under the existing topic-related stories and newly arrived stories in order to fulfill the incremental expansion of topic information and anti-noise processing.The experiment results show that this method improves the topic association precision and solve the topic evolution deviation problem effectively.