A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Text classification using string kernels
The Journal of Machine Learning Research
The Journal of Machine Learning Research
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Semantic Kernels for Text Classification Based on Topological Measures of Feature Similarity
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Learning to identify emotions in text
Proceedings of the 2008 ACM symposium on Applied computing
Tree kernels for semantic role labeling
Computational Linguistics
SWAT-MP: the SemEval-2007 systems for task 5 and task 14
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
UA-ZBSA: a headline emotion classification through web information
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
UPAR7: a knowledge-based system for headline sentiment tagging
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Identifying expressions of emotion in text
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
Using a heterogeneous dataset for emotion analysis in text
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Sentiment analysis of Twitter data
LSM '11 Proceedings of the Workshop on Languages in Social Media
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
From once upon a time to happily ever after: Tracking emotions in mail and books
Decision Support Systems
Portable features for classifying emotional text
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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In this paper, we systematically analyze the effect of incorporating different levels of syntactic and semantic information on the accuracy of emotion recognition from text. We carry out the evaluation in a supervised learning framework, and employ tree kernel functions as an intuitive and effective way to generate different feature spaces based on structured representations of the input data. We compare three different formalisms to encode syntactic information enriched with semantic features. These features are obtained from hand-annotated resources as well as distributional models. For the experiments, we use three datasets annotated according to the same set of emotions. Our analysis indicates that shallow syntactic information can positively interact with semantic features. In addition, we show how the three datasets can hardly be combined to learn more robust models, due to inherent differences in the linguistic properties of the texts or in the annotation.