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Information Processing and Management: an International Journal
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Machine Learning - Special issue on learning with probabilistic representations
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
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ECML '98 Proceedings of the 10th European Conference on Machine Learning
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ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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ICALT '04 Proceedings of the IEEE International Conference on Advanced Learning Technologies
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ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Investigating Human Tutor Responses to Student Uncertainty for Adaptive System Development
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
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Computer Speech and Language
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Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
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ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Recognizing Affect from Linguistic Information in 3D Continuous Space
IEEE Transactions on Affective Computing
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ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
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COST'11 Proceedings of the 2011 international conference on Cognitive Behavioural Systems
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In a large scale study on 843 transcripts of Technology, Entertainment and Design TED talks, the authors address the relation between word usage and categorical affective ratings of lectures by a large group of internet users. Users rated the lectures by assigning one or more predefined tags which relate to the affective state evoked in the audience e. g., 'fascinating', 'funny', 'courageous', 'unconvincing' or 'long-winded'. By automatic classification experiments, they demonstrate the usefulness of linguistic features for predicting these subjective ratings. Extensive test runs are conducted to assess the influence of the classifier and feature selection, and individual linguistic features are evaluated with respect to their discriminative power. In the result, classification whether the frequency of a given tag is higher than on average can be performed most robustly for tags associated with positive valence, reaching up to 80.7% accuracy on unseen test data.