Authors: Danilo Cavaliere; Sabrina Senatore
Addresses: Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (Sa), Italy ' Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (Sa), Italy
Abstract: Social media has become a fulcrum for sharing information on everyday-life events: people, companies, and organisations express opinions about new products, political and social situations, football matches, and concerts. The recognition of feelings and reactions to events from social networks requires dealing with great amounts of data streams, especially for tweets, to investigate the main sentiments and opinions that justify some reactions. This paper presents an emotion-based classification model to extract feelings from tweets related to an event or a trend, described by a hashtag, and build an emotional concept ontology to study human reactions to events in a context. From the tweet analysis, terms expressing a feeling are selected to build a topological space of emotion-based concepts. The extracted concepts serve to train a multi-class SVM classifier that is used to perform soft classification aimed at identifying the emotional reactions towards events. Then, an ontology allows arranging classification results, enriched with additional DBpedia concepts. SPARQL queries on the final knowledge base provide specific insights to explain people's reactions towards events. Practical case studies and test results demonstrate the applicability and potential of the approach.
Keywords: emotion soft classification; SVM; emotional concept ontology; RDF; SKOS; SPARQL queries; sentiment analysis; tweets; simplicial complex; emotional concept extraction.
International Journal of Metadata, Semantics and Ontologies, 2021 Vol.15 No.1, pp.23 - 38
Received: 30 May 2020
Accepted: 08 Jan 2021
Published online: 10 Aug 2021 *