Uncovering deep user context from blogs
Proceedings of the second workshop on Analytics for noisy unstructured text data
Multi-scale characterization of social network dynamics in the blogosphere
Proceedings of the 17th ACM conference on Information and knowledge management
Negation, contrast and contradiction in text processing
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Detecting controversial events from twitter
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Survey on mining subjective data on the web
Data Mining and Knowledge Discovery
Towards mega-modeling: a walk through data analysis experiences
ACM SIGMOD Record
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Our study addresses the problem of large-scale contradiction detection and management, from data extracted from the Web. We describe the first systematic solution to the problem, based on a novel statistical measure for contradictions, which exploits first- and second-order moments of sentiments. Our approach enables the interactive analysis and online identification of contradictions under multiple levels of time granularity. The proposed algorithm can be used to analyze and track opinion evolution over time and to identify interesting trends and patterns. It uses an incrementally updatable data structure to achieve computational efficiency and scalability. Experiments with real datasets show promising time performance and accuracy.