A Hybrid Model for Emotion Detection from Text

A Hybrid Model for Emotion Detection from Text

Samar Fathy (Faculty of Computers and Information, Helwan University, Helwan, Egypt), Nahla El-Haggar (Faculty of Computers and Information, Helwan University, Helwan, Egypt) and Mohamed H. Haggag (Faculty of Computers and Information, Helwan University, Helwan, Egypt)
Copyright: © 2017 |Pages: 17
DOI: 10.4018/IJIRR.2017010103


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.
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Currently there are three approaches dominating the emotion detection task; keyword based, learning based and hybrid based approach.

The limitations of current emotion detection approaches from text are: ambiguity in keyword definitions, incapability of recognizing sentences without keywords, lack of linguistic information, difficulties in determining emotion indicators, and over-simplified emotion categories. These limitations lead to an open problem in emotion detection from text.

Keyword Based Approach

This approach depends on the presence of keywords and involves pre-processing with a parser and emotion dictionary. It is easy to implement and straight forward since it involves identifying words to search for in text.

This approach has been applied in chat systems by (Chunling, Prendinger, &Ishizuka, 2005). Their chat system displays emotion using an avatar. It is also applied by (Hancock, Landrigan, & Silver, 2007). They introduce a laboratory controlled online chat experiment to enact sadness and happiness and reporting strategies that people employ to express emotions in text. (Li, Pang, & Guo, 2007) also applied this approach. They introduce the emotion detection incorporating personality factor in chatting system to improve accuracy.

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