A Hybrid Model for Emotion Detection from Text

Samar Fathy, Nahla El-Haggar and Mohamed H. Haggag
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Samar Fathy: Faculty of Computers and Information, Helwan University, Helwan, Egypt
Nahla El-Haggar: Faculty of Computers and Information, Helwan University, Helwan, Egypt
Mohamed H. Haggag: Faculty of Computers and Information, Helwan University, Helwan, Egypt

International Journal of Information Retrieval Research (IJIRR), 2017, vol. 7, issue 1, 32-48

Abstract: Emotions can be judged by a combination of cues such as speech facial expressions and actions. Emotions are also articulated by text. This paper shows a new hybrid model for detecting emotion from text which depends on ontology with keywords semantic similarity. The text labelled with one of the six basic Ekman emotion categories. The main idea is to extract ontology from input sentences and match it with the ontology base which created from simple ontologies and the emotion of each ontology. The ontology extracted from the input sentence by using a triplet (subject, predicate, and object) extraction algorithm, then the ontology matching process is applied with the ontology base. After that the emotion of the input sentence is the emotion of the ontology which it matches with the highest score of matching. If the extracted ontology doesn't match with any ontology from the ontology base, then the keyword semantic similarity approach used. The suggested approach depends on the meaning of each sentence, the syntax and semantic analysis of the context.

Date: 2017
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