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Machine learning and natural language processing of social media data for event detection in smart cities / Andrei Hodorog in Sustainable Cities and Society, vol 85 (October 2022)  

Public [article]  inSustainable Cities and Society > vol 85 (October 2022) . - n° 104026 Titre : Machine learning and natural language processing of social media data for event detection in smart cities Type de document : Article/Communication Auteurs : Andrei Hodorog, Auteur ; Ioan Petri, Auteur ; yacine Rezgui, Auteur Année de publication : 2022 Article en page(s) : n° 104026 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] apprentissage automatique [Termes IGN] classification bayesienne [Termes IGN] détection d'événement [Termes IGN] données issues des réseaux sociaux [Termes IGN] outil d'aide à la décision [Termes IGN] régression multiple [Termes IGN] taxinomie [Termes IGN] traitement du langage naturel [Termes IGN] ville intelligente Résumé : (auteur) Social media data analysis in a smart city context can represent an efficacious instrument to inform decision making. The manuscript strives to leverage the power of Natural Language Processing (NLP) techniques applied to Twitter messages using supervised learning to achieve real-time automated event detection in smart cities. A semantic-based taxonomy of risks is devised to discover and analyse associated events from data streams, with a view to: (i) read and process, in real-time, published texts (ii) classify each text into one representative real-world category (iii) assign a citizen satisfaction value to each event. To select the language processing models striking the best balance between accuracy and processing speed, we conducted a pre-emptive evaluation, comparing several baseline language models formerly employed by researchers for event classification. A heuristic analysis of several smart cities and community initiatives was conducted, with a view to define real-world scenarios as basis for determining correlations between two or more co-occurring event types and their associated levels of citizen satisfaction, while further considering environmental factors. Based on Multiple Regression Analysis (MRA), we established the relationships between scenario variables, obtaining a variance of 60%–90% between the dependent and independent variables. The selected combination of supervised NLP techniques leverages an accuracy of 88.5%. We found that all regression models had at least one variable below the 0.05 threshold of the , therefore at least one statistically significant independent variable. These findings ultimately illustrate how citizens, taking the role of active social sensors, can yield vital data that authorities can use to make educated decisions and sustainably construct smarter cities. Numéro de notice : A2022-764 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scs.2022.104026 Date de publication en ligne : 02/07/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104026 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101785 [article]