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Title: :  Toxic Comments Identification Using Machine Learning
PaperId: :  19653
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 9 Issue 2 2023
DUI:    16.0415/IJARIIE-19653
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Mohammed GouseB V Raju Institute Of Technology
kurma SrikanthB V Raju Institute Of Technology
DR. B. VENKATESHWARA RAOB V Raju Institute Of Technology

Abstract

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

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IJARIIE 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 Volume 9 Issue 2 2023 Page 1731-1735
MLA 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 9.2(2023) : 1731-1735.
APA Mohammed Gouse, kurma Srikanth, & DR. B. VENKATESHWARA RAO. (2023). Toxic Comments Identification Using Machine Learning. International Journal Of Advance Research And Innovative Ideas In Education, 9(2), 1731-1735.
Chicago 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 9, no. 2 (2023) : 1731-1735.
Oxford 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, 2023, p. 1731-1735. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/Toxic_Comments_Identification_Using_Machine_Learning_ijariie19653.pdf (Accessed : ).
Harvard Mohammed Gouse, kurma Srikanth, and DR. B. VENKATESHWARA RAO. (2023) 'Toxic Comments Identification Using Machine Learning', International Journal Of Advance Research And Innovative Ideas In Education, 9(2), pp. 1731-1735IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/Toxic_Comments_Identification_Using_Machine_Learning_ijariie19653.pdf (Accessed : )
IEEE 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, Mar-App 2023. [Online]. Available: http://ijariie.com/AdminUploadPdf/Toxic_Comments_Identification_Using_Machine_Learning_ijariie19653.pdf [Accessed : ].
Turabian 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 [Online]. volume 9 number 2 ().
Vancouver 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 [Internet]. 2023 [Cited : ]; 9(2) : 1731-1735. Available from: http://ijariie.com/AdminUploadPdf/Toxic_Comments_Identification_Using_Machine_Learning_ijariie19653.pdf
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