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Hate and Aggression Detection in Social Media Over Hindi English Language

Hate and Aggression Detection in Social Media Over Hindi English Language

Kapil Pareek, Arjun Choudhary, Ashish Tripathi, K. K. Mishra, Namita Mittal
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 20
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781683181019|DOI: 10.4018/IJSSCI.300357
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MLA

Pareek, Kapil, et al. "Hate and Aggression Detection in Social Media Over Hindi English Language." IJSSCI vol.14, no.1 2022: pp.1-20. http://doi.org/10.4018/IJSSCI.300357

APA

Pareek, K., Choudhary, A., Tripathi, A., Mishra, K. K., & Mittal, N. (2022). Hate and Aggression Detection in Social Media Over Hindi English Language. International Journal of Software Science and Computational Intelligence (IJSSCI), 14(1), 1-20. http://doi.org/10.4018/IJSSCI.300357

Chicago

Pareek, Kapil, et al. "Hate and Aggression Detection in Social Media Over Hindi English Language," International Journal of Software Science and Computational Intelligence (IJSSCI) 14, no.1: 1-20. http://doi.org/10.4018/IJSSCI.300357

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Abstract

In today’s time, everyone is familiar with social media platforms. It is quite helpful in connecting people. It has many advantages and some disadvantages too. Currently, in social media, hate and aggression have become a huge problem. On these platforms, many people make inflammatory posts targeting any person or society by using code mixed language, due to which many problems arise in the society. At the current time, much research work is being done on English language-related social media posts. The authors have focused on code mixed language. Authors have also tried to focus on sentences that do not use abusive words but contain hatred-related remarks. In this research, authors have used Natural Language Processing (NLP). Authors have applied Fasttext word embedding to the dataset. Fasttext is a technique of NLP. Deep learning (DL) classification algorithms were applied thereafter. In this research, two classifications have been used i.e. Convolutional Neural Network (CNN) and Bidirectional LSTM (Bi-LSTM).

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