Abstract: Emotion Detection from text is a very important area of natural language processing. This paper shows a new method for emotion detection from text which depends on ontology. This method is depending on ontology extraction from the input sentence by using a triplet extraction algorithm by the OpenNLP parser, then make an ontology matching with the ontology base that we created by similarity and word sense disambiguation. This ontology base consists of ontologies and the emotion label related to each one. We choose the emotion label of the sentence with the highest score of matching. If the extracted ontology doesn’t match any ontology from the ontology base we use the keyword-based approach. This method doesn’t depend only on keywords like previous approaches; it depends on the meaning of sentence words and the syntax and semantic analysis of the context.

Mohamed Haggag, Samar Fathy and Nahla Elhaggar, “Ontology-Based Textual Emotion Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 6(9), 2015. http://dx.doi.org/10.14569/IJACSA.2015.060932

@article{Haggag2015,
title = {Ontology-Based Textual Emotion Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2015.060932},
url = {http://dx.doi.org/10.14569/IJACSA.2015.060932},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {9},
author = {Mohamed Haggag and Samar Fathy and Nahla Elhaggar}
}