Abstract
With time, people have graduated towards expressing themselves through the medium of the Internet, specifically over social media platforms. It creates scenarios where a user may, knowingly or unknowingly, make a reference or comment which may be derogatory to an individual and/or a section of society. It may hinder observant from participating in the conversation or even stop visiting the website altogether, thereby hurting the prospects of the website owner. A human may easily detect such infringements; however it is a huge pursuit for a computer. In this paper, we present a text classification method to classify the comments as insulting or otherwise. For this purpose, we extract features using various methods and enrich them using k-skip-n-grams to achieve a good set of features for the task. Further, feature selection is applied to obtain a subset of relevant features. Finally, a competitive and collaborative analysis of five different machine learning methods (classifiers) is presented to show that a collaborative model is a clear winner. It is a step towards making a machine learning based automated system to detect the insulting comments in the conversations.
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Gupta, A., Singh, P.K. (2018). Detection of Insulting Comments in Online Discussion. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Hybrid Intelligent Systems. HIS 2017. Advances in Intelligent Systems and Computing, vol 734. Springer, Cham. https://doi.org/10.1007/978-3-319-76351-4_12
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DOI: https://doi.org/10.1007/978-3-319-76351-4_12
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