Verbal offense detection in social network comments using novel fusion approach

Article type: Research Article

Authors: Bchir, Ouiem | Ben Ismail, Mohamed Maher^{; *}

Affiliations: College of Computer and Information Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: We propose a framework for automatic verbal offense detection in social network comments. The proposed approach adapts a possibilistic based fusion method to different regions of the feature space in order to classify social network comments as insult or not. The proposed algorithm is formulated mathematically through the minimization of some objective functions. It combines context identification and multi-algorithm fusion criteria into a joint objective function. The optimization is intended to produce contexts as compact clusters in subspaces of the high-dimensional feature space via possibilistic unsupervised learning and feature discrimination. The clustering component associates a degree of typicality with each data sample in order to identify and reduce the influence of noise points and outliers. Also, the approach provides optimal fusion parameters for each context. Our initial experiments on synthetic datasets and standard SMS datasets indicate that the proposed fusion approach outperforms individual classifiers. Finally, the proposed system is assessed using real collection of social network comments, and compared to state-of-the-art fusion technique.

Keywords: Verbal offense detection, supervised learning local fusion, social network comments

DOI: 10.3233/AIC-150674

Journal: AI Communications, vol. 28, no. 4, pp. 765-780, 2015