The Journal of Machine Learning Research
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Machine learning techniques for business blog search and mining
Expert Systems with Applications: An International Journal
Design and development of a mobile peer-to-peer social networking application
Expert Systems with Applications: An International Journal
Topic-link LDA: joint models of topic and author community
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A latent topic model for linked documents
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Detecting cyber security threats in weblogs using probabilistic models
PAISI'07 Proceedings of the 2007 Pacific Asia conference on Intelligence and security informatics
Probabilistic techniques for corporate blog mining
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Detecting novel business blogs
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
In-depth behavior understanding and use: The behavior informatics approach
Information Sciences: an International Journal
Dimensionality reduction techniques for blog visualization
Expert Systems with Applications: An International Journal
A data-centric approach to feed search in blogs
International Journal of Web Engineering and Technology
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Blog mining is an important area of behavior informatics because produces effective techniques for analyzing and understanding human behaviors from social media. In this paper, we propose the blogger-link-topic model for blog mining based on the multiple attributes of blog content, bloggers, and links. In addition, we present a unique blog classification framework that computes the normalized document-topic matrix, which is applied our model to retrieve the classification results. After comparing the results for blog classification on real-world blog data, we find that our blogger-link-topic model outperforms the other techniques in terms of overall precision and recall. This demonstrates that additional information contained in blog-specific attributes can help improve blog classification and retrieval results.