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Exploiting ontology information in fuzzy SVM social media profile classification

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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.

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Notes

  1. http://nlp.uned.es/replab2014/

  2. https://pan.webis.de/clef17/pan17-web/author-identification.html

  3. https://www.kaggle.com/arminehn/disasteraccident-sources

  4. https://data.world/crowdflower/gender-classifier-data

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  1. MARS Research Laboratory, University of Sousse, Sousse, Tunisia

    Olfa Mabrouk, Lobna Hlaoua & Mohamed Nazih Omri

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  1. Olfa Mabrouk
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  2. Lobna Hlaoua
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  3. Mohamed Nazih Omri
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Correspondence to Olfa Mabrouk.

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Mabrouk, O., Hlaoua, L. & Omri, M.N. Exploiting ontology information in fuzzy SVM social media profile classification. Appl Intell 51, 3757–3774 (2021). https://doi.org/10.1007/s10489-020-01939-2

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