Research On News Topic Detection Based On Incremental Clustering

With the rapid development of Internet technology, Internet has established itself as the fourth largest media dominance, become the major carrier of releasing information, getting information and transporting information to the public. Due to timeliness and universality of news reports, news portals have become its vanguard of news report. In addition, the point of view and quantity of the news reports also can reflects current public opinion and the main social contradiction to a certain extent. Therefore, it is vital and urgent to do research on discovering hot topics on Internet and study how to find that. It has been found that in finding news topic research, timeliness and definiteness of network information are the most important aspects. The core technology is clustering algorithm used on news topics. Considering clustering algorithm accuracy and objectiveness of analysis, this paper studies two key technologies which are incremental clustering algorithm and machine translation for real practical meaning.This paper mainly includes three aspects. The first one is doing Chinese Word Segmentation after getting reports on each portal news sites, aiming to make the segmentation more suitable for next steps. This paper improves the traditional segmentation methods, obtaining accurate and adaptive results. The second aspect is improved traditional single-pass incremental clustering algorithm on accuracy and recall rate of clustering, which is the focus of this article. The third one is improved the objectivity and persuasive of news topic through combining news information clustering results from present domestic news sites with machine translation technology and overseas research on news report.