Open AccessPerformance of Text Classification Methods in Detection of Hate Speech in Media
Author(s) -
Srinedhi Thanvanthri,
Shivani Ramakrishnan
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.40567
Subject(s) - popularity , computer science , voice activity detection , social media , sentence , encoder , speech recognition , emotion detection , natural language processing , artificial intelligence , internet privacy , speech processing , world wide web , psychology , social psychology , emotion recognition , operating system
With the increased popularity of social media sites like Twitter and Instagram over the years, it has become easier for users of the sites to remain anonymous while taking part in hate speech against various peoples and communities. As a result, in an effort to curb such hate speech online, detection of the same has gained a lot more attention of late. Since curbing the growing amount of hate speech online by manual methods is not feasible, detection and control via Natural Language Processing and Deep Learning methods has gained popularity. In this paper, we evaluate the performance of a sequential model with the Universal Sentence Encoder against the RoBERTa method on different datasets for hate speech detection. The result of this study has shown a greater performance overall from using a Sequential model with a multilingual USE layer. Keywords: Hate Speech Detection, RoBERTa, Universal Sentence Encoder, Sequential model.