C4.5: programs for machine learning
C4.5: programs for machine learning
Social presence and group attraction: exploring the effects of awareness systems in the home
Cognition, Technology and Work
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watching together: integrating text chat with video
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Proceedings of the first workshop on Online social networks
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Social TV: toward content navigation using social awareness
Proceedings of the 8th international interactive conference on Interactive TV&Video
SentiTVchat: sensing the mood of social-TV viewers
Proceedings of the 10th European conference on Interactive tv and video
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Social TV was named one of the ten most important emerging technologies in 2010 by the MIT Technology Review. Manufacturers of set-top boxes and televisions have recently started to integrate access to social networks into their products. Some of these systems allow users to read microblogging messages related to the TV program they are currently watching. However, such systems suffer from low precision and recall when they use the title of the show as keywords when retrieving messages, without any additional filtering. We propose a bootstrapping approach to collecting microblogging messages related to a given TV program. We start with a small set of annotated data, in which, for a given show and a candidate message, we annotate the pair to be relevant or irrelevant. From this annotated data set, we train an initial classifier. The features are designed to capture the association between the TV program and the message. Using our initial classifier and a large dataset of unlabeled messages we derive broader features for a second classifier to further improve precision.