A model of textual affect sensing using real-world knowledge
Proceedings of the 8th international conference on Intelligent user interfaces
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Using appraisal groups for sentiment analysis
Proceedings of the 14th ACM international conference on Information and knowledge management
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
Adding predicate argument structure to the Penn TreeBank
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Opinion mining of customer feedback data on the web
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Inter-coder agreement for computational linguistics
Computational Linguistics
Just how mad are you? finding strong and weak opinion clauses
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
Computational Linguistics
Emotional sequencing and development in fairy tales
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Character-based kernels for novelistic plot structure
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
EGVE - JVRC'10 Proceedings of the 16th Eurographics conference on Virtual Environments & Second Joint Virtual Reality
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Emotion analysis (EA) is a rapidly developing area in computational linguistics. An EA system can be extremely useful in fields such as information retrieval and emotion-driven computer animation. For most EA systems, the number of emotion classes is very limited and the text units the classes are assigned to are discrete and predefined. The question we address in this paper is whether the set of emotion categories can be enriched and whether the units to which the categories are assigned can be more flexibly defined. We present an experiment showing how an annotation task can be set up so that untrained participants can perform emotion analysis with high agreement even when not restricted to a predetermined annotation unit and using a rich set of emotion categories. As such it sets the stage for the development of more complex EA systems which are closer to the actual human emotional perception of text.