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
Splog detection using self-similarity analysis on blog temporal dynamics
AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
Blog search and mining in the business domain
Proceedings of the 2007 international workshop on Domain driven data mining
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
Detecting cyber security threats in weblogs using probabilistic models
PAISI'07 Proceedings of the 2007 Pacific Asia conference on Intelligence and security informatics
Detecting novel business blogs
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Dimensionality reduction for blog tag mining
International Journal of Web Engineering and Technology
Chinese categorization and novelty mining
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Using latent topics to enhance search and recommendation in Enterprise Social Software
Expert Systems with Applications: An International Journal
Finding keywords in blogs: Efficient keyword extraction in blog mining via user behaviors
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Blog mining addresses the problem of mining information from blog data. Although mining blogs may share many similarities to Web and text documents, existing techniques need to be reevaluated and adapted for the multidimensional representation of blog data, which exhibit dimensions not present in traditional documents, such as tags. Blog tags are semantic annotations in blogs which can be valuable sources of additional labels for the myriad of blog documents. In this paper, we present a tag-topic model for blog mining, which is based on the Author-Topic model and Latent Dirichlet Allocation. The tag-topic model determines the most likely tags and words for a given topic in a collection of blog posts. The model has been successfully implemented and evaluated on real-world blog data.