Indian Journal of Science and Technology
Year: 2019, Volume: 12, Issue: 45, Pages: 1-13
Usman Naseem1*, Shah Khalid Khan2, Madiha Farasat3 and Farasat Ali4
1School of Computer Science, University of Technology Sydney, Australia; [email protected]
2School of Engineering, RMIT, Australia; [email protected]
3School of Electrical and Data Engineering, University of Technology Sydney, Australia;
4School of Electrical Engineering, UET, Pakistan; [email protected]
*Author for correspondence
School of Computer Science, University of Technology Sydney, Australia.
Email: [email protected]
Objectives: To provide an organised 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 methods and research conducted on the types of Abusive language used in social media, why it is important? 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 organised review of past methodologies, including methods, important 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 idea which is characterised in numerous stages ad settings. This area of study has an obvious potential for societal effect, especially in digital media and online networks. A crucial step in propelling automatic hate speech detection is the advancement and systemisation of common assets, for example, clarified data sets in numerous dialects, rules, and calculations. Conclusion: This survey study contains all the relevant references related to detection of abusive language on social media using NLP and machine learning methods. Ultimately, it can be as source of references to the other researchers in finding the literatures that are relevant to their research area in the detection of Abusive language on Twitter.
Keywords: Abusive Language, Natural Language Processing, Social media analysis, Text Classification and Analysis
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