A model of textual affect sensing using real-world knowledge
Proceedings of the 8th international conference on Intelligent user interfaces
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning to identify emotions in text
Proceedings of the 2008 ACM symposium on Applied computing
Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
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
Hierarchical approach to emotion recognition and classification in texts
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Learning and evaluation in the presence of class hierarchies: application to text categorization
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Emotion detection state of the art
Proceedings of the CUBE International Information Technology Conference
Emotional sentence identification in a story
Proceedings of the Workshop at SIGGRAPH Asia
Unsupervised Emotion Detection from Text Using Semantic and Syntactic Relations
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Text-based emotion classification using emotion cause extraction
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
We explore the task of automatic classification of texts by the emotions expressed. Our novel method arranges neutrality, polarity and emotions hierarchically. We test the method on two datasets and show that it outperforms the corresponding "flat" approach, which does not take into account the hierarchical information. The highly imbalanced structure of most of the datasets in this area, particularly the two datasets with which we worked, has a dramatic effect on the performance of classification. The hierarchical approach helps alleviate the effect.