Feature selection for text categorization on imbalanced data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Random k-Labelsets: An Ensemble Method for Multilabel Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
Affect Analysis of Web Forums and Blogs Using Correlation Ensembles
IEEE Transactions on Knowledge and Data Engineering
Emotion Classification of Online News Articles from the Reader's Perspective
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
SemEval-2007 task 14: affective text
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
A Comparison of Multi-label Feature Selection Methods using the Problem Transformation Approach
Electronic Notes in Theoretical Computer Science (ENTCS)
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Multiple emotions are often evoked in readers in response to text stimuli like news article. In this paper, we present a novel method for classifying news sentences into multiple emotion categories using Multi-Label K Nearest Neighbor classification technique. The emotion data consists of 1305 news sentences and the emotion classes considered are disgust, fear, happiness and sadness. Words and polarity of subject, verb and object of the sentences and semantic frames have been used as features. Experiments have been performed on feature comparison and feature selection.