Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Modern Information Retrieval
ICALT '01 Proceedings of the IEEE International Conference on Advanced Learning Technologies
Toward an Affect-Sensitive AutoTutor
IEEE Intelligent Systems
Automatic detection of learner's affect from conversational cues
User Modeling and User-Adapted Interaction
Learning to identify emotions in text
Proceedings of the 2008 ACM symposium on Applied computing
IEEE Transactions on Knowledge and Data Engineering
SemEval-2007 task 14: affective text
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Identifying expressions of emotion in text
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
A high-order hidden Markov model for emotion detection from textual data
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
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
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In this paper we present an evaluation of new techniques for automatically detecting emotions in text. The study estimates categorical model and dimensional model for the recognition of four affective states: Anger, Fear, Joy, and Sadness that are common emotions in three datasets: SemEval-2007 "Affective Text", ISEAR (International Survey on Emotion Antecedents and Reactions), and children's fairy tales. In the first model, WordNet-Affect is used as a linguistic lexical resource and three dimensionality reduction techniques are evaluated: Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), and Non-negative Matrix Factorization (NMF). In the second model, ANEW (Affective Norm for English Words), a normative database with affective terms, is employed. Experiments show that a categorical model using NMF results in better performances for SemEval and fairy tales, whereas a dimensional model performs better with ISEAR.