{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:53:42Z","timestamp":1761126822089,"version":"3.38.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T00:00:00Z","timestamp":1725408000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T00:00:00Z","timestamp":1725408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soc. Netw. Anal. Min."],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Deep visual data analysis from social network has become an increasingly important area of research. In fact, this form of assessment makes it viable to recognize new information on social users which incorporates emotions. In order to recognize users\u2019 emotions and other latent attributes, most of the existing approaches have used textual data and have obtained accurate results. However, little attention has been paid to visual data that have become increasingly popular in recent years.This work describes how to develop a conceptual representation model for social network analysis and social emotion analysis based on the integration of fuzzy logic and ontological modeling. The primary aim is to create an ontology that can extract new information about a social user\u2019s mood, which can be classified as panic, no-panic, or neutral. Fuzzy logic is necessary to deal with subjective data, as users share imprecise and vague data on their social profiles. Fuzzy logic has been observed as a successful method to capture the expression of emotions due to the fuzzy nature of emotions and the ambiguous definitions of emotion words. The proposed work investigate the role of fuzzy logic in social network analysis. This study simulate a fuzzy deep system integrated with ontology for classifying social visual data (shared images) into panic, no-panic or neutral classes in order to determine the social users\u2019 stress intensity. The Social distancing and the huge amount of shared data in Tunisia were calculated to demonstrate this classification. The experiments performed in this paper aim to create not only a novel annotated visual database named visual panic database, but also a new semantic model for modeling users\u2019 profiles, in social network, based on the combination between ontology and deep learning techniques. In this context, we will work in the future on the combination between a user\u2019s visual and textual data in order to improve the performance of this recognition. The proposed fuzzy system reflected the viral proliferation in stressed users and achieved an accuracy of 87%.<\/jats:p>","DOI":"10.1007\/s13278-024-01277-y","type":"journal-article","created":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T16:03:59Z","timestamp":1725465839000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Fuzzy deep visual panic detection"],"prefix":"10.1007","volume":"14","author":[{"given":"Onsa","family":"Lazzez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tarek M.","family":"Hamdani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Habib","family":"Chabchoub","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khmaies","family":"Ouhada","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adel M.","family":"Alimi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,4]]},"reference":[{"key":"1277_CR1","first-page":"491","volume":"6","author":"CC Aggarwal","year":"2022","unstructured":"Aggarwal CC (2022) Opinion mining and sentiment analysis. Mach Learn 6:491\u2013514","journal-title":"Mach Learn"},{"key":"1277_CR2","doi-asserted-by":"crossref","unstructured":"Ahire V, Swati B (2022) Emotion detection from social media using machine learning techniques: a survey. In: Applied information processing systems, vol 83\u201392. Springer, Singapore","DOI":"10.1007\/978-981-16-2008-9_8"},{"key":"1277_CR3","unstructured":"Alrabbaa C et al (2022) On the eve of true explainability for OWL ontologies: description logic proofs with Evee and Evonne. In: Proceedings of DL 22"},{"key":"1277_CR4","first-page":"127","volume":"8","author":"E Carneiro","year":"2022","unstructured":"Carneiro E, Guedes GP, Belloze KT (2022) A review of ontology-based approaches for sentiment analysis: possible improvements on the Brazilian affective computing scenario. Ontobras 8:127\u2013136","journal-title":"Ontobras"},{"key":"1277_CR5","first-page":"685","volume":"13","author":"T Cassia","year":"2021","unstructured":"Cassia T et al (2021) Foundational ontologies meet ontology matching: a survey. Semant Web 13:685\u2013704","journal-title":"Semant Web"},{"key":"1277_CR6","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1080\/10408363.2020.1783198","volume":"57","author":"M Ciotti","year":"2020","unstructured":"Ciotti M, Ciccozzi M, Terrinoni A, Jiang WC, Wang CB, Sergio B (2020) The COVID-19 pandemic. Crit Rev Clin Labo Sci 57:365\u2013388","journal-title":"Crit Rev Clin Labo Sci"},{"key":"1277_CR7","first-page":"171","volume-title":"Incorporating student opinion into opinion mining. Open world learning","author":"G Hillaire","year":"2022","unstructured":"Hillaire G et al (2022) Incorporating student opinion into opinion mining. Open world learning, 1st edn. Routledge, London, pp 171\u2013185","edition":"1"},{"issue":"1","key":"1277_CR8","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1108\/JHTI-10-2022-0487","volume":"7","author":"MS Hossain","year":"2024","unstructured":"Hossain MS, Rahman MF (2024) Detection of readers\u2019 emotional aspects and thumbs-up empathy reactions towards reviews of online travel agency apps. J Hospital Tour Insights 7(1):142\u2013171. https:\/\/doi.org\/10.1108\/JHTI-10-2022-0487","journal-title":"J Hospital Tour Insights"},{"key":"1277_CR9","doi-asserted-by":"publisher","first-page":"50","DOI":"10.3390\/bs12020050","volume":"12","author":"T Huawei","year":"2022","unstructured":"Huawei T, Siddik AB, Masukujjaman M (2022) Factors affecting the repurchase intention of organic tea among millennial consumers: an empirical study. Behav Sci 12:50","journal-title":"Behav Sci"},{"issue":"1","key":"1277_CR10","doi-asserted-by":"publisher","first-page":"102751","DOI":"10.1016\/j.ipm.2021.102751","volume":"59","author":"Y Jie","year":"2022","unstructured":"Jie Y et al (2022) Social media data analytics for business decision making system to competitive analysis. Inf Process Manag 59(1):102751","journal-title":"Inf Process Manag"},{"key":"1277_CR11","doi-asserted-by":"publisher","first-page":"20727","DOI":"10.1109\/ACCESS.2022.3149214","volume":"10","author":"H Juanpablo","year":"2022","unstructured":"Juanpablo H et al (2022) Adaptive multimodal emotion detection architecture for social robots. IEEE Access 10:20727\u201320744","journal-title":"IEEE Access"},{"key":"1277_CR12","doi-asserted-by":"publisher","first-page":"27","DOI":"10.4018\/978-1-6684-6303-1.ch003","volume":"8","author":"V Karas","year":"2022","unstructured":"Karas V, Schuller BW (2022) Deep learning for sentiment analysis: an overview and perspectives. Res Anthol Implement Sentiment Anal Across Multiple Discip 8:27\u201362","journal-title":"Res Anthol Implement Sentiment Anal Across Multiple Discip"},{"key":"1277_CR13","doi-asserted-by":"publisher","first-page":"1846","DOI":"10.4018\/978-1-6684-6303-1.ch098","volume":"8","author":"R Kaur","year":"2022","unstructured":"Kaur R, Sandeep K (2022) Multimodal sentiment analysis: a survey and comparison. Res Anthol Implement Sentiment Anal Across Multiple Discipl 8:1846\u20131870","journal-title":"Res Anthol Implement Sentiment Anal Across Multiple Discipl"},{"issue":"9","key":"1277_CR14","doi-asserted-by":"publisher","first-page":"11989","DOI":"10.1007\/s11042-020-10480-w","volume":"81","author":"R Kumar","year":"2022","unstructured":"Kumar R, Satheesh S et al (2022) Exploration of sentiment analysis and legitimate artistry for opinion mining. Multimed Tools Appl 81(9):11989\u201312004","journal-title":"Multimed Tools Appl"},{"key":"1277_CR15","first-page":"11989","volume":"8","author":"RS Kumar","year":"2021","unstructured":"Kumar RS, Saviour Devaraj AF, Rajeswari M, Julie EG, Robinson YH, Shanmuganathan V (2021) Exploration of sentiment analysis and legitimate artistry for opinion mining. Multimed Tools Appl 8:11989","journal-title":"Multimed Tools Appl"},{"key":"1277_CR16","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1016\/S2215-0366(22)00156-0","volume":"9","author":"R Larsen Rasmus","year":"2022","unstructured":"Larsen Rasmus R et al (2022) More phenomenology in psychiatry? Applied ontology as a method towards integration. Lancet Psychiatry 9:751\u2013758","journal-title":"Lancet Psychiatry"},{"key":"1277_CR17","doi-asserted-by":"publisher","first-page":"40913","DOI":"10.1007\/s11042-023-14806-2","volume":"82","author":"O Lazzez","year":"2023","unstructured":"Lazzez O, Qahtani AM, Alsufyani A et al (2023) DeepVisInterests: deep data analysis for topics of interest prediction. Multimed Tools Appl 82:40913\u201340936","journal-title":"Multimed Tools Appl"},{"issue":"3","key":"1277_CR18","first-page":"1","volume":"31","author":"B Lin","year":"2022","unstructured":"Lin B, Cassee N, Serebrenik A, Bavota G, Novielli N, Lanza M (2022) Opinion mining for software development: a systematic literature review. ACM Trans Softw Eng Methodol 31(3):1\u201341","journal-title":"ACM Trans Softw Eng Methodol"},{"key":"1277_CR19","doi-asserted-by":"publisher","first-page":"1608","DOI":"10.3390\/app12031608","volume":"12","author":"MC May","year":"2022","unstructured":"May MC et al (2022) Ontology-based production simulation with OntologySim. Appl Sci 12:1608","journal-title":"Appl Sci"},{"issue":"3","key":"1277_CR20","doi-asserted-by":"publisher","first-page":"1608","DOI":"10.3390\/app12031608","volume":"12","author":"MC May","year":"2022","unstructured":"May MC, Kiefer L, Kuhnle A, Lanza G (2022) Ontology-based production simulation with OntologySim. Appl Sci 12(3):1608","journal-title":"Appl Sci"},{"key":"1277_CR21","first-page":"1","volume":"14","author":"H Mehdi","year":"2022","unstructured":"Mehdi H et al (2022) Technical methods for social robots in museum settings: an overview of the literature. Int J Soc Robot 14:1\u201320","journal-title":"Int J Soc Robot"},{"issue":"1","key":"1277_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-021-00855-8","volume":"12","author":"C Messaoudi","year":"2022","unstructured":"Messaoudi C, Guessoum Z, Ben Romdhane L (2022) Opinion mining in online social media: a survey. Social Netw Anal Min 12(1):1\u20138","journal-title":"Social Netw Anal Min"},{"key":"1277_CR23","doi-asserted-by":"crossref","unstructured":"Messaoudi C, Zahia G, Lotfi BR (2022) A deep learning model for opinion mining in twitter combining text and emojis. In: 26th International conference on knowledge-based and intelligent information engineering systems (KES 2022), vol 12, pp 1\u201318","DOI":"10.1016\/j.procs.2022.09.321"},{"key":"1277_CR24","doi-asserted-by":"crossref","unstructured":"Ombabi AH et al (2017) Deep learning framework based on Word2Vec and CNNfor users interests classification. In: 2017 Sudan conference on computer science and information technology (SCCSIT). IEEE","DOI":"10.1109\/SCCSIT.2017.8293054"},{"key":"1277_CR25","doi-asserted-by":"crossref","unstructured":"Onsa L, Wael O, Alimi AM (2016) Age, gender, race and smile prediction based on social textual and visual data analyzing. ISDA, pp 206\u2013215","DOI":"10.1007\/978-3-319-53480-0_21"},{"key":"1277_CR26","doi-asserted-by":"crossref","unstructured":"Onsa L, Wael O, Alimi AM (2016) Understand Me if You Can! Global soft biometrics recognition from social visual data. HIS, pp 527\u2013538","DOI":"10.1007\/978-3-319-52941-7_52"},{"key":"1277_CR27","unstructured":"Onsa L, Wael O, Alimi AM (2018) DeepVisInterests: CNN-ontology prediction of users interests from social images. CoRR abs\/1811.10920"},{"key":"1277_CR28","unstructured":"Onsa L, Wael O, Alimi AM (2018) DeepVisInterests: CNN-ontology prediction of users interests from social images. CoRR abs\/1811.10920"},{"key":"1277_CR29","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3390\/s22103749","volume":"22","author":"M Quiroz","year":"2022","unstructured":"Quiroz M et al (2022) Group emotion detection based on social robot perception. Sensors 22:10","journal-title":"Sensors"},{"key":"1277_CR30","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1080\/19331681.2021.1928579","volume":"19","author":"S Rasmus","year":"2022","unstructured":"Rasmus S, Bossetta M (2022) FBAdLibrarian and Pykognition: open science tools for the collection and emotion detection of images in Facebook political ads with computer vision. J Inf Technol Polit 19:118\u2013128","journal-title":"J Inf Technol Polit"},{"key":"1277_CR31","doi-asserted-by":"publisher","first-page":"104258","DOI":"10.1016\/j.jbi.2022.104258","volume":"137","author":"AH Saffar","year":"2022","unstructured":"Saffar AH, Tiffany KM, Bahadorreza O (2022) Textual emotion detection in health: advances and applications. J Biomed Inf 137:104258","journal-title":"J Biomed Inf"},{"key":"1277_CR32","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1080\/19331681.2021.1928579","volume":"19","author":"R Schm\u00f8kel","year":"2022","unstructured":"Schm\u00f8kel R, Bossetta M (2022) FBAdLibrarian and Pykognition: open science tools for the collection and emotion detection of images in Facebook political ads with computer vision. J Inf Technol Polit 19:118\u2013128","journal-title":"J Inf Technol Polit"},{"key":"1277_CR33","first-page":"74","volume":"17","author":"PC Sherimon","year":"2021","unstructured":"Sherimon PC, Sherimon V, Preethii SP, Nair RV, Mathew R (2021) A systematic review of clinical decision support systems in Alzheimer\u2019s disease domain. Biomed Eng 17:74\u201390","journal-title":"Biomed Eng"},{"key":"1277_CR34","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1504\/IJDATS.2022.124766","volume":"14","author":"AB Siddique","year":"2022","unstructured":"Siddique AB, Mahfuzulhoq C (2022) A machine learning-based approach to predict university students\u2019 depression pattern and mental healthcare assistance scheme using Android application. Int J Data Anal Tech Strateg 14:122\u2013139","journal-title":"Int J Data Anal Tech Strateg"},{"key":"1277_CR35","doi-asserted-by":"publisher","first-page":"8","DOI":"10.3390\/app12083709","volume":"12","author":"C Singh","year":"2022","unstructured":"Singh C, Imam T, Wibowo S, Grandhi S (2022) A deep learning approach for sentiment analysis of COVID- 19 reviews. Appl Sci 12:8","journal-title":"Appl Sci"},{"key":"1277_CR36","doi-asserted-by":"publisher","first-page":"3709","DOI":"10.3390\/app12083709","volume":"12","author":"C Singh","year":"2022","unstructured":"Singh C, Imam T, Wibowo S, Grandhi S (2022) A deep learning approach for sentiment analysis of COVID-19 reviews. Appl Sci 12(8):3709","journal-title":"Appl Sci"},{"key":"1277_CR37","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.neucom.2013.01.024","volume":"117","author":"B Souhir","year":"2013","unstructured":"Souhir B et al (2013) A hybrid learning algorithm for evolving flexible beta basis function neural tree model. Neurocomputing 117:107\u2013117","journal-title":"Neurocomputing"},{"key":"1277_CR38","doi-asserted-by":"publisher","first-page":"3085","DOI":"10.3390\/ijerph19053085","volume":"19","author":"SB Sukhavasi","year":"2022","unstructured":"Sukhavasi SB et al (2022) A hybrid model for driver emotion detection using feature fusion approach. Int J Environ Res Public Health 19:3085","journal-title":"Int J Environ Res Public Health"},{"key":"1277_CR39","doi-asserted-by":"publisher","first-page":"685","DOI":"10.3233\/SW-210447","volume":"13","author":"C Trojahn","year":"2022","unstructured":"Trojahn C, Vieira R, Schmidt D, Pease A, Guizzardi G (2022) Foundational ontologies meet ontology matching: a survey. Semant Web 13:685\u2013704","journal-title":"Semant Web"},{"key":"1277_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1061\/(ASCE)ME.1943-5479.0000970","volume":"38","author":"X Wan","year":"2022","unstructured":"Wan X et al (2022) Online public opinion mining for large cross-regional projects: case study of the south-to-north water diversion project in China. J Manag Eng 38:1","journal-title":"J Manag Eng"},{"key":"1277_CR41","first-page":"1","volume":"38","author":"X Wan","year":"2022","unstructured":"Wan X et al (2022) Online public opinion mining for large cross-regional projects: case study of the south-to-north water diversion project in China. J Mana Eng 38:1","journal-title":"J Mana Eng"},{"key":"1277_CR42","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1504\/IJCC.2022.124796","volume":"11","author":"A Waseem","year":"2022","unstructured":"Waseem A, Alahmadi Ahmed H (2022) Ontology building for patient bioinformatics of the smart card domain: implementation using owl. Int J Cloud Comput 11:316\u2013329","journal-title":"Int J Cloud Comput"},{"key":"1277_CR43","doi-asserted-by":"crossref","unstructured":"Zhou D et al (2022) Ontology reshaping for knowledge graph construction: applied on Bosch welding case. In: International semantic web conference. Springer, Cham","DOI":"10.1007\/978-3-031-19433-7_44"},{"issue":"1","key":"1277_CR44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41377-021-00680-w","volume":"11","author":"C Zuo","year":"2022","unstructured":"Zuo C, Qian J, Feng S, Yin W, Li Y, Fan C (2022) Deep learning in optical metrology: a review. Light Sci Appl 11(1):1\u201354","journal-title":"Light Sci Appl"}],"container-title":["Social Network Analysis and Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13278-024-01277-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13278-024-01277-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13278-024-01277-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T14:34:15Z","timestamp":1740494055000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13278-024-01277-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,4]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1277"],"URL":"https:\/\/doi.org\/10.1007\/s13278-024-01277-y","relation":{},"ISSN":["1869-5469"],"issn-type":[{"type":"electronic","value":"1869-5469"}],"subject":[],"published":{"date-parts":[[2024,9,4]]},"assertion":[{"value":"11 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"180"}}