On-line new event detection and tracking
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Improving text categorization methods for event tracking
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Web classification using support vector machine
Proceedings of the 4th international workshop on Web information and data management
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On the bursty evolution of blogspace
WWW '03 Proceedings of the 12th international conference on World Wide Web
The Journal of Machine Learning Research
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
A cross-collection mixture model for comparative text mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining correlated bursty topic patterns from coordinated text streams
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Blog classification using tags: an empirical study
ICADL'07 Proceedings of the 10th international conference on Asian digital libraries: looking back 10 years and forging new frontiers
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In the era of Web 2.0, the number of blogs has explosively increased. With the appearance of social network services, blogs has become the places for sharing professional knowledge and personal branding. So, in order to understand the trends of topics or to analyze the content of blogs, the time sensitive topic extraction and topic change analysis is important and necessary. In the previous studies, most of topic extraction models extracted topic words independently from each time slice and tried to combine those. However, these methods did not show a good performance in analyzing topic trends because the topics extracted from time slices are independent. To cope with this problem, we propose a term frequency smoothing method which weaves time slices so that the more related topics are extracted from each time slice and a better topic trend analysis is generated. In order to extract topics from smoothed term frequencies, LDA, a generative topic model, is adopted. The evaluation of the proposed method on IT blogs shows that it can effectively discover quite meaningful topic patterns and topic words.