Computing Temporal Trends in Web Documents

Most existing methods of web content mining assume a static nature of the web documents. This approach is inadequate for long-term monitoring and analysis of the web content, since both the users' interests and the content of most web sites are subject to continuous changes over time. In this research, we are interested in developing computationally intelligent and efficient text mining techniques that will enable continuous comparison between documents provided by the same source (website, institute, organization, cult, author etc.) or viewed by the same group of users (e.g., university students) and timely detection of temporal trends in those documents. Our approach builds upon the recently developed methodology for fuzzy comparison of frequency distributions. The proposed techniques are evaluated on a real-world stream of web traffic.