Authors:
Ovidiu Şerban
1
;
Alexandre Pauchet
2
and
Horia F. Pop
3
Affiliations:
1
LITIS - INSA de Rouen and Babeş-Bolyai University, France
;
2
LITIS - INSA de Rouen, France
;
3
Babeş-Bolyai University, Romania
Keyword(s):
Natural Language Processing, Machine Learning, Affective Computing, Text Mining, Emotion Detection
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Knowledge Discovery and Information Retrieval
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Methods
;
Natural Language Processing
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
;
Theory and Methods
Abstract:
Affective Computing is one of the fields used by computer scientists to transfer the knowledge from psychology to the Human-Machine Interaction research field, while offering a better understanding on Human to Human Interaction. Several approaches have been tried in the area, like text and voice techniques to discover emotions. Since the classification problem is not typical, the difficulty is increased by the fuzziness of the data sets. Our paper proposes a method that aims at a better recognition rate of human emotions. Our model is based on the Self-Organizing Maps algorithm and it can be applied on short texts with a high degree of affective content. It is designed to be integrated into an Embodied Conversational Agent.