An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
GATE: an architecture for development of robust HLT applications
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Bolasso: model consistent Lasso estimation through the bootstrap
Proceedings of the 25th international conference on Machine learning
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Access: news and blog analysis for the social sciences
Proceedings of the 19th international conference on World wide web
Flu detector: tracking epidemics on twitter
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Detecting events in a million New York times articles
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
NOAM: news outlets analysis and monitoring system
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Lydia: a system for large-scale news analysis
SPIRE'05 Proceedings of the 12th international conference on String Processing and Information Retrieval
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The strong trend towards the automation of many aspects of scientific enquiry and scholarship has started to affect also the social sciences and even the humanities. Several recent articles have demonstrated the application of pattern analysis techniques to the discovery of non-trivial relations in various datasets that have relevance for social and human sciences, and some have even heralded the advent of "Computational Social Sciences" and "Culturomics". In this review article I survey the results obtained over the past 5 years at the Intelligent Systems Laboratory in Bristol, in the area of automating the analysis of news media content. This endeavor, which we approach by combining pattern recognition, data mining and language technologies, is traditionally a part of the social sciences, and is normally performed by human researchers on small sets of data. The analysis of news content is of crucial importance due to the central role that the global news system plays in shaping public opinion, markets and culture. It is today possible to access freely online a large part of global news, and to devise automated methods for large scale constant monitoring of patterns in content. The results presented in this survey show how the automatic analysis of millions of documents in dozens of different languages can detect non-trivial macro-patterns that could not be observed at a smaller scale, and how the social sciences can benefit from closer interaction with the pattern analysis, artificial intelligence and text mining research communities.