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Semantic analysis based on ontology and deep learning for a chatbot to assist persons with personality disorders on Twitter

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  • Mourad Ellouze
  • Lamia Hadrich Belguith

Abstract

This paper presents a chatbot taking advantage of semantic analysis based on ontology and deep learning techniques for ensuring the monitoring of Twitter users with personality disorders during the period of COVID-19. The monitoring provided by our work consists of (i) removing inappropriate tweets from the newsfeed of the sick person according to their state, (ii) providing via a chatbot an answer to the sick person in the form of another tweet that can help him to overcome their concerns about a problem related to the epidemic. Our approach was started by detecting people having personality disorders on Twitter, followed by detecting their behaviour towards COVID-19 expressed in tweets posted in relation to this epidemic. After that, moving to perform the filtration and the recommendation tasks of tweets based on a semantic analysis. Our semantic analysis is achieved at first by querying an ontology based on a comparison taking into account concepts and behaviour expressed. Then, via a deep learning approach in order to resolve untreated cases by the ontology. For the evaluation part, we obtained an F-measure value equals to 72% for the task of filtering inappropriate tweets and 75% for the task of recommended tweet.

Suggested Citation

  • Mourad Ellouze & Lamia Hadrich Belguith, 2025. "Semantic analysis based on ontology and deep learning for a chatbot to assist persons with personality disorders on Twitter," Behaviour and Information Technology, Taylor & Francis Journals, vol. 44(10), pages 2140-2159, June.
    • Handle: RePEc:taf:tbitxx:v:44:y:2025:i:10:p:2140-2159
    DOI: 10.1080/0144929X.2023.2272757
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