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Emotional Concept Extraction Through Ontology-Enhanced Classification

  • Danilo CavaliereEmail author
  • Sabrina Senatore
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1057)

Abstract

Capturing emotions affecting human behavior in social media bears strategic importance in many decision-making fields, such as business and public policy, health care, and financial services, or just social events. This paper introduces an emotion-based classification model to analyze the human behavior in reaction to some event described by a tweet trend. From tweets analysis, the model extracts terms expressing emotions, and then, it builds a topological space of emotion-based concepts. These concepts enable the training of the multi-class SVM classifier to identify emotions expressed in the tweets. Classifier results are “softly” interpreted as a blending of several emotional nuances which thoroughly depicts people’s feeling. An ontology model captures the emotional concepts returned by classification, with respect to the tweet trends. The associated knowledge base provides human behavior analysis, in response to an event, by a tweet trend, by SPARQL queries.

Keywords

Sentiment analysis Simplicial Complex SVM Ontology 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Dipartimento di Ingegneria dell’Informazione ed Elettrica e Matematica Applicata - DIEMUniversitá degli Studi di SalernoFiscianoItaly

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