A model of textual affect sensing using real-world knowledge
Proceedings of the 8th international conference on Intelligent user interfaces
Automatic labeling of semantic roles
Computational Linguistics
Semantic Role Parsing: Adding Semantic Structure to Unstructured Text
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Emotion recognition from text using semantic labels and separable mixture models
ACM Transactions on Asian Language Information Processing (TALIP)
The role of semantic roles in disambiguating verb senses
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Emotions from text: machine learning for text-based emotion prediction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Unsupervised named-entity extraction from the Web: An experimental study
Artificial Intelligence
Automatic event-level textual emotion sensing using mutual action histogram between entities
Expert Systems with Applications: An International Journal
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
IEEE Transactions on Affective Computing
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Automatic emotion sensing in textual data is crucial for the development of intelligent interfaces in interactive computer applications. This paper reports a high-precision, domain-independent approach for automatic emotion sensing for "events" embedded in sentences. The proposed approach is based on the common action distribution between the subject and object of an event. We have incorporated semantic labeling and web-based text mining techniques, together with a number of reference entity pairs and hand-crafted emotion generation rules to realize an event emotion detection system. Moreover, a hybrid emotion detection engine is presented by incorporating a set of predefined emotion keywords and the proposed event-level emotion detection engine. The evaluation outcome reveals a rather satisfactory result with about 73% accuracy for detecting the Happy, Sad, Fear, Angry, Surprise, Disgust, and Neutral.