Un-Compromised Credibility: Social Media based Multi-Class Hate Speech Classification for Text
Priyanka R. Telshinge, Dr. Mangesh D Salunke
Hate Speech, Natural Language Processing, Classification, Social Media Micro blogs, Twitter Dataset.
Not just in person but also via the internet, people are committing more crimes related to hate speech, which has been on the rise in recent years. A variety of factors have contributed to this result. People are more inclined to participate in hostile behaviour online because of the anonymity provided by the internet and social networks in particular. On the other hand, people are more likely to engage in hostile behaviour offline. On the other hand, more and more people are using the internet to share their opinions, which contributes to the proliferation of hate speech. Because this kind of prejudiced speech has the potential to be so detrimental to society, implementing detection and prevention strategies can be beneficial for governments as well as social media companies. By providing a comprehensive evaluation of the research that has been carried out on the topic through the course of this survey, we make a contribution toward resolving this conundrum. This task benefited from the employment of multiple sophisticated and non-linear models, and CAT Boost fared the best as a result of the application of latent semantic analysis (LSA) for the purpose of dimensionality reduction.
Article Details
Unique Paper ID: 155161

Publication Volume & Issue: Volume 9, Issue 1

Page(s): 71 - 75
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