Abstract
This paper aims to propose a novel approach to automatically detect verbal offense in social network comments. It relies on a local approach that adapts the fusion method to different regions of the feature space in order to classify comments from social networks as insult or not. The proposed algorithm is formulated mathematically through the minimization of some objective function. It combines context identification and multi-algorithm fusion criteria into a joint objective function. This optimization is intended to produce contexts as compact clusters in subspaces of the high-dimensional feature space via possibilistic unsupervised learning and feature weighting. Our initial experiments have indicated that the proposed fusion approach outperforms individual classifiers and the global fusion method. Also, in order to validate the obtained results, we compared the performance of the proposed approach with related fusion methods.
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Acknowledgments
This work was supported by the Research Center of College of Computer and Information Sciences, King Saud University (Project RC131013). The authors are grateful for this support.
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Ben Ismail, M.M., Bchir, O. (2015). Insult Detection in Social Network Comments Using Possibilistic Based Fusion Approach. In: Lee, R. (eds) Computer and Information Science. Studies in Computational Intelligence, vol 566. Springer, Cham. https://doi.org/10.1007/978-3-319-10509-3_2
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DOI: https://doi.org/10.1007/978-3-319-10509-3_2
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