Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Using Error-Correcting Codes for Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Memory-Based Language Processing (Studies in Natural Language Processing)
Memory-Based Language Processing (Studies in Natural Language Processing)
Error Correcting Codes from Quasi-Hadamard Matrices
WAIFI '07 Proceedings of the 1st international workshop on Arithmetic of Finite Fields
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Toward plot units: automatic affect state analysis
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
On the use of homogenous sets of subjects in deceptive language analysis
EACL 2012 Proceedings of the Workshop on Computational Approaches to Deception Detection
Modelling fixated discourse in chats with cyberpedophiles
EACL 2012 Proceedings of the Workshop on Computational Approaches to Deception Detection
Self-disclosure and relationship strength in Twitter conversations
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
On the impact of sentiment and emotion based features in detecting online sexual predators
WASSA '12 Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis
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We introduce a new emotion classification task based on Leary's Rose, a framework for interpersonal communication. We present a small dataset of 740 Dutch sentences, outline the annotation process and evaluate annotator agreement. We then evaluate the performance of several automatic classification systems when classifying individual sentences according to the four quadrants and the eight octants of Leary's Rose. SVM-based classifiers achieve average F-scores of up to 51% for 4-way classification and 31% for 8-way classification, which is well above chance level. We conclude that emotion classification according to the Interpersonal Circumplex is a challenging task for both humans and machine learners. We expect classification performance to increase as context information becomes available in future versions of our dataset.