Affective computing
A model of textual affect sensing using real-world knowledge
Proceedings of the 8th international conference on Intelligent user interfaces
Latent Semantic Mapping: Principles And Applications (Synthesis Lectures on Speech and Audio Processing)
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
Learning to identify emotions in text
Proceedings of the 2008 ACM symposium on Applied computing
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Text-to-text semantic similarity for automatic short answer grading
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
SemEval-2007 task 14: affective text
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Expressing degree of activation in synthetic speech
IEEE Transactions on Audio, Speech, and Language Processing
Affect analysis of text using fuzzy semantic typing
IEEE Transactions on Fuzzy Systems
From once upon a time to happily ever after: tracking emotions in novels and fairy tales
LaTeCH '11 Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities
Tracking sentiment in mail: how genders differ on emotional axes
WASSA '11 Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis
From once upon a time to happily ever after: Tracking emotions in mail and books
Decision Support Systems
Demonstration of IlluMe: creating ambient according to instant message logs
ACL '12 Proceedings of the ACL 2012 System Demonstrations
Emotion detection in suicide notes
Expert Systems with Applications: An International Journal
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Though data-driven in nature, emotion analysis based on latent semantic analysis still relies on some measure of expert knowledge in order to isolate the emotional keywords or keysets necessary to the construction of affective categories. This makes it vulnerable to any discrepancy between the ensuing taxonomy of affective states and the underlying domain of discourse. This paper proposes a more general strategy which leverages two distincts semantic levels, one that encapsulates the foundations of the domain considered, and one that specifically accounts for the overall affective fabric of the language. Exposing the emergent relationship between these two levels advantageously informs the emotion classification process. Empirical evidence suggests that this is a promising solution for automatic emotion detection in text.