Design And Implementation Of The Hot-Topic From Web News Detecting System

During the past years, Web which is seen as a new media has developed rapidly and a large amount of information has also appeared. Many platforms are growing up day by day, such as portal sites, forums, communities. Many applications are spreading rapidly, such as short message, instant message, blog, micro-blogging. So the Web has become an important platform for all the cyber citizens to voice opinion and to show emotion freely. Besides the hot-topics cyber citizens concern can easily develop into public sentiment.These hot-topics are likely to make great influence to the social if they are used improperly by some bad persons. So it is urgent to take some guidance at the beginning of the shaping of the hot-topics. My work described in this thesis is just the foundation to achieve this goal. Based on the words split from the title and the content of the Webs news, the highlight of this thesis is a deep analysis of the feature of the Web news. I have adopted different way to deal with the corresponding type of Web news. Besides I have chosen a proper text clustering algorithm which is called shared nearest neighbor to find all the hot-topics from a certain collection of Web news using the form of Web page. Furthermore, compared with the result classified by the manual work, I have also taken a test which showed the high precision and the normal recall of the system on finding the hot-topics.The thesis firstly discusses the background and the requirement of the system. Secondly, concentrating on the feature of the Web news, it describes how to analyze, design and implement the system of the hot-topic detection from Web news. Finally, it also shows the whole procedure of the test and evaluation of the system. The result is relatively good and it concludes some experience.