The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Journal of Machine Learning Research
WWW '05 Proceedings of the 14th international conference on World Wide Web
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Predicting News Story Importance Using Language Features
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Discovering authoritative news sources and top news stories
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
Ranking web news via homepage visual layout and cross-site voting
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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Online news is a major source of information for many people. The overwhelming amount of new articles published every day makes it necessary to filter out unimportant ones and detect ground breaking new articles. In this paper, we propose the use of Latent Dirichlet Allocation (LDA) to find the hidden factors of important news stories. These factors are then used to train a Support Vector Machine (SVM) to classify new news items as they appear. We compare our results with SVMs based on a bag-of-words approach and other language features. The advantage of a LDA processing is not only a better accuracy in predicting important news, but also a better interpretability of the results. The latent topics show directly the important factors of a news story.