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Authors: Mourad Ellouze ; Seifeddine Mechti and Lamia Hadrich Belguith

Affiliation: ANLP Group MIRACL Laboratory, FSEGS, University of Sfax-Tunisia, Tunisia

Keyword(s): Paranoid Personality Disorder Detection, Deep Learning Architecture, Symptoms and Disease Detection, Text Mining, Twitter.

Abstract: In this paper, we propose an approach based on artificial intelligence (AI) and text mining techniques for measuring the degrees of appearance of symptoms related to paranoid disease in Twitter users. This operation will then help in the detection of people suffering from paranoid personality disorder in a manner that provides justifiable and explainable results by answering the question: What factors lead us to believe that this person suffers from paranoid personality disorder? These challenges were achieved using a deep neural approach, including: (i) CNN layers for features extraction step from the textual part, (ii) BiLSTM layer to classify the intensity of symptoms by preserving long-term dependencies, (iii) an SVM classifier to detect users with paranoid personality disorder based on the degree of symptoms obtained from the previous layer. According to this approach, we get an F-measure rate equivalent to 71% for the average measurement of the degree of each symptom and 65% fo r detecting paranoid people. The results achieved motivate and encourage researchers to improve them in view of the relevance and importance of this research area. (More)

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Paper citation in several formats:
Ellouze, M.; Mechti, S. and Hadrich Belguith, L. (2022). Deep Learning CNN-LSTM Approach for Identifying Twitter Users Suffering from Paranoid Personality Disorder. In Proceedings of the 17th International Conference on Software Technologies - ICSOFT; ISBN 978-989-758-588-3; ISSN 2184-2833, SciTePress, pages 612-621. DOI: 10.5220/0011322300003266

@conference{icsoft22,
author={Mourad Ellouze. and Seifeddine Mechti. and Lamia {Hadrich Belguith}.},
title={Deep Learning CNN-LSTM Approach for Identifying Twitter Users Suffering from Paranoid Personality Disorder},
booktitle={Proceedings of the 17th International Conference on Software Technologies - ICSOFT},
year={2022},
pages={612-621},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011322300003266},
isbn={978-989-758-588-3},
issn={2184-2833},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Software Technologies - ICSOFT
TI - Deep Learning CNN-LSTM Approach for Identifying Twitter Users Suffering from Paranoid Personality Disorder
SN - 978-989-758-588-3
IS - 2184-2833
AU - Ellouze, M.
AU - Mechti, S.
AU - Hadrich Belguith, L.
PY - 2022
SP - 612
EP - 621
DO - 10.5220/0011322300003266
PB - SciTePress

- Science and Technology Publications, Lda.
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