Toxic Comments Identification Using Machine Learning

Mohammed Gouse; kurma Srikanth; DR. B. VENKATESHWARA RAO

Toxic Comments Identification Using Machine Learning

April 2023

Vol-9, Issue-2

Paper ID: 19653

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Computer Engineering

Keywords

Deep Learning Convolutional Neural Networks Long Short-Term Memory Networks Supervised Learning Data Set Data Preprocessing Text Normalization Tokenization Toxicity Lemmatization Stop words.

Abstract

Online forums and social media platforms have provided individuals with the means to put forward their thoughts and freely express their opinion on various issues and incidents. In some cases, these online comments contain explicit language which may hurt the readers. Comments containing explicit language can be classified into myriad categories such as Toxic, Obscene, Threat, Insult, Nationalist, Racist, Sexist, Hate speech. The threat of abuse and harassment means that many people stop expressing themselves and give up on seeking different opinions. The goal is to create a model that can predict if input text is inappropriate(toxic). Here we use Natural Language Toolkit(NLTK), LSTM, LSTM-CNN for classification of types of toxic comments.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

#	Name	Institute / Affiliation
1	Mohammed Gouse	B V Raju Institute Of Technology
2	kurma Srikanth	B V Raju Institute Of Technology
3	DR. B. VENKATESHWARA RAO	B V Raju Institute Of Technology

How to Cite

Use the following formats to cite this article in your research.

APA Style

Gouse, Mohammed, Srikanth, kurma, & RAO, DR. B. VENKATESHWARA (2023). Toxic Comments Identification Using Machine Learning. International Journal of Advance Research and Innovative Ideas In Education, 9(2), 1731-1735.

MLA Style

Gouse, Mohammed, et al. "Toxic Comments Identification Using Machine Learning." International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 2, 2023, pp. 1731-1735.

IEEE Style

Mohammed Gouse, kurma Srikanth, and DR. B. VENKATESHWARA RAO, "Toxic Comments Identification Using Machine Learning," International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 2, pp. 1731-1735, 2023.

Vancouver Style

Gouse Mohammed, Srikanth kurma, RAO DR. B. VENKATESHWARA. Toxic Comments Identification Using Machine Learning. International Journal of Advance Research and Innovative Ideas In Education. 2023;9(2):1731-1735.

Harvard Style

Gouse, Mohammed, Srikanth, kurma, & RAO, DR. B. VENKATESHWARA (2023) 'Toxic Comments Identification Using Machine Learning', International Journal of Advance Research and Innovative Ideas In Education, 9(2), pp. 1731-1735.

Chicago Style

Gouse, Mohammed, kurma Srikanth, and DR. B. VENKATESHWARA RAO. "Toxic Comments Identification Using Machine Learning." International Journal of Advance Research and Innovative Ideas In Education 9, no. 2 (2023): 1731-1735.

Turabian Style

Gouse, Mohammed, kurma Srikanth, and DR. B. VENKATESHWARA RAO. "Toxic Comments Identification Using Machine Learning." International Journal of Advance Research and Innovative Ideas In Education 9, no. 2 (2023): 1731-1735.