The nature of statistical learning theory
The nature of statistical learning theory
Information Retrieval
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
The Wisdom of Crowds
Technologies That Make You Smile: Adding Humor to Text-Based Applications
IEEE Intelligent Systems
On Text-based Mining with Active Learning and Background Knowledge Using SVM
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Making computers laugh: investigations in automatic humor recognition
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Getting serious about the development of computational humor
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The Journal of Machine Learning Research
Communications of the ACM
Dusting for science: motivation and participation of digital citizen science volunteers
Proceedings of the 2011 iConference
Learning facial attributes by crowdsourcing in social media
Proceedings of the 20th international conference companion on World wide web
A hybrid AIS-SVM ensemble approach for text classification
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
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Jokes classification is an intrinsically subjective and complex task, mainly due to the difficulties related to cope with contextual constraints on classifying each joke. Nowadays people have less time to devote to search and enjoy humour and, as a consequence, people are usually interested on having a set of interesting filtered jokes that could be worth reading, that is with a high probability of make them laugh. In this paper we propose a crowdsourcing based collective intelligent mechanism to classify humour and to recommend the most interesting jokes for further reading. Crowdsourcing is becoming a model for problem solving, as it revolves around using groups of people to handle tasks traditionally associated with experts or machines. We put forward an active learning Support Vector Machine (SVM) approach that uses crowdsourcing to improve classification of user custom preferences. Experiments were carried out using the widely available Jester jokes dataset, with encouraging results.