APPLICABILITY OF SVM, RANDOM FOREST, AND DECISION TREE ALGORITHMS IN TEXT MINING FOR EFFECTIVE DETECTION OF ABUSIVE LANGUAGE IN ASSIGNED DATASETS

Romharsh Mittal

Vol. 4, Jan-Dec 2018

Page Number: 263-279

Abstract:

Objectives: To provide related literature on the detection of Abusive language on Twitter using natural language processing (NLP). Methods: In this study, the survey has been conducted on different techniques and research done on the types of Abusive language used in social media, why it is essential? How it has been detected in real-time social media platforms and the performance metrics that are used by researchers in evaluating the performance of the detection of abusive language on Twitter by the users. Results: Giving an organized review of past methodologies, including methods, essential features, and core algorithms, this study arranges and depicts the present condition about this area. The study also talks about the intricacy of hate speech ideas, which is characterized by numerous stages of ad settings. This area of research has obvious potential for societal effects, especially in digital media and online networks. A crucial step in propelling automatic hate speech detection is the advancement and systematization of shared assets, for example, clarified data sets in numerous dialects, rules, and calculations. Conclusion: This survey study contains all the relevant references related to the detection of abusive language on social media using NLP and machine learning methods. Ultimately, it can be a source of reference to the other researchers in finding the pieces of literature that are relevant to their research area in the detection of Abusive language on Twitter.

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