DOI: 10.55524/ijirem.2023.10.1.22 | DOI URL: https://doi.org/10.55524/ijirem.2023.10.1.22 Crossref
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
Article Tools: Print the Abstract | Indexing metadata | How to cite item | Email this article | Post a Comment
Anvita Karne , Vaishnavi Thakare, Sonal Fatangare
"Hate speech" refers to objectionable statements that may endanger societal harmony and targets a group or a person based on inborn qualities (such as race, religion, or gender). The issue of hate speech has been continuously growing on social media platforms lately. Our research focuses on creating a robust comment classifier that will categorize comments according to their toxicity. A series of activities or issues with getting a software to automatically categorize input comments into categories depending on the toxicity of the comment are referred to as comment classification. Our model combines LSTM and BERT with additional language processing methods. The application determines the category of toxic comments and shows the proportion of toxicity using an algorithmic technique.
Student, Department of Computer Science & Engineering, Rasiklal M. Dhariwal School of Engineering, Pune, India
No. of Downloads: 14 | No. of Views: 110
SK. Heena Kauser, I. Meghana, A. Mounika, B. Rajeswari, Dr. A. Seshagiri Rao.
December 2022 - Vol 9, Issue 6
Rajan Kumar Yadav, Munish Saran, Pranjal Maurya, Sangeeta Devi, Upendra Nath Tripathi.
October 2022 - Vol 9, Issue 5
D. Janardhan Reddy, V. Gopi Krishna, Sk. JIlani Basha, R. Pavan Kumar, K. Manohara Rao.
August 2022 - Vol 9, Issue 4