Ontology-Based Textual Emotion Detection

Abstract

Abstract-EmotionDetection 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.

Enhancement of Text Based Emotion Recognition Performances Using Word Clusters

“…The generalization capability of these approaches is not of very high quality, because it is difficult and expensive to create an emotion lexicon and associated rules that are appropriate for all the domains. An attempt to identify a variety of emotions in the text, usually focusing on the six basic emotions of anger, fear, disgust, joy, sadness, and surprise had been pointed out by Haggag et al in [11]. These are called the refined sentiment detection/emotion recognition.…”

Feature Extraction Enchancement in Users’ Attitude Detection

“…However, they disagree on which emotions and how many should be classified as basic emotions. Ekman, Izard, and Pultchick lists which are shown in Table 1 [16] are the most common lists of emotions used in emotion detection methods. Emotions detection (ED) can be considered as a sentiment analysis task.…”