Exploiting ontology information in fuzzy SVM social media profile classification
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Exploiting ontology information in fuzzy SVM social media profile classification Olfa Mabrouk1 · Lobna Hlaoua1 · Mohamed Nazih Omri1 Accepted: 11 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Nowadays, social media like Twitter, Facebook, blogs, and LinkedIn are considered the most used sources of information, while at the same time being the most visited and most used sources of disinformation. These can have a negative impact on several areas and on our minds, hence on our behavior. It is obvious that this disinformation is closely related to the profiles of the authors of this information. The purpose of author profiling is to analyze the texts published by the authors in order to determine their profile category. A wide range of methods for selecting statistical characteristics and machine learning has been studied in recent years in order to automatically classify this information. However, these main methods of selecting statistical characteristics and machine learning used for this purpose have not proven their great performance in the processing of data from social networks. The main contribution of this article consists in integrating the semantic component, which has not been taken into account in the main approaches studied in the literature, as additional functionalities enabling the identification of relevant information. Our hypothesis is that the concepts and the relationships between these concepts tend to have a more coherent correlation with relevant and irrelevant information, and can therefore increase the discriminating power of classifiers. The semantic approach proposed revolves around an ontology combined with the linear SVM classifier and then with the fuzzy SVM classifier. The experimental study carried out, on the different collections of Twitter profiles. On our approach and on the main approaches to the literature that we have studied, as well as the analysis of the results obtained. The results we have clearly show the limits of these studied approaches and confirm the performance of our approach, as well as the efficiency of the integration of the semantic component in the categorization of Twitter profiles. Keywords Fuzzy SVM · Multi-class social media · Profile categorization
1 Introduction The rise of social media provides new models of communication and social relationships. These media allow to hide the real profile of the users who interact and generate information. Therefore, the possibility of knowing social media users’ traits on the basis of what they share is a field of Olfa Mabrouk
[email protected] Lobna Hlaoua [email protected] Mohamed Nazih Omri [email protected] 1
MARS Research Laboratory, University of Sousse, Sousse, Tunisia
growing interest named author profiling. Twitter has become one of the most popular Social Networks. It is popular among many types of users from various countries. Thus, it has been used by many types of users: people, organizations, etc to share their messages and opinions. Twitt
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