Call for Papers:Vol.10 Issue.6

Submission
Last date		 30-Dec-2024
Acceptance Status In One Day
Paper Publish In Two Days
Submit ManuScript

News & Updates

Submit Article

Dear Authors, Article publish in our journal for Volume-10,Issue-6. For article submission on below link: Submit Manuscript


Join As Board

Dear Reviewer, You can join our Reviewer team without given any charges in our journal. Submit Details on below link: Join As Board


Paper Publication Charges

IJARIIE APP
Download Android App

For Authors

Archives

IJARIIE Board

Downloads

Android App

Download IJARIIE APP

Authors

Title: :  Detecting Toxic comments By Using LSTM-CNN Model
PaperId: :  24077
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 3 2024
DUI:    16.0415/IJARIIE-24077
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Bhalshankar S.TMIT College Of Railway Engineering & Research , Barshi
Shubham GuptaMIT College Of Railway Engineering & Research , Barshi
Akhilesh YadavMIT College Of Railway Engineering & Research , Barshi
Vaishnav MahadikMIT College Of Railway Engineering & Research , Barshi
Prajval PalwadeMIT College Of Railway Engineering & Research , Barshi
Prathamesh KhondhareMIT College Of Railway Engineering & Research , Barshi

Abstract

Computer Engineering
Toxic Comments, Convolutional Neural Network, Toxic Detection, Deep Learning, Text Classification, Machine Learning, Model Evaluation, Feature Extraction, Data Preprocessing
Nowadays users leave numerous comments on different social networks, news portals, and forums. Some of the comments are toxic or abusive. Due to the number of comments, it is unfeasible to manually moderate them, so most of the systems use some kind of automatic discovery of toxicity using machine learning models. In this work, we performed a systematic review o The state-of-the-art in toxic comment classification using machine learning methods. We have studied the impact of Support vector machines (SVM), Long Short- Term Memory Networks (LSTM), Convolutional Neural Networks (CNN), and Multilayer Perceptron (MLP) methods, in combination with word and character level embeddings, on identifying toxicity in text. We evaluated our approaches on Wikipedia comments from the Kaggle Toxic Comments Classification Challenge dataset. Regarding character-level classification, our best results occurred when using a CNN model.

Citations

Copy and paste a formatted citation or use one of the links to import into a bibliography manager and reference.

IJARIIE Bhalshankar S.T, Shubham Gupta, Akhilesh Yadav, Vaishnav Mahadik, Prajval Palwade, and Prathamesh Khondhare. "Detecting Toxic comments By Using LSTM-CNN Model" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 3 2024 Page 2830-2841
MLA Bhalshankar S.T, Shubham Gupta, Akhilesh Yadav, Vaishnav Mahadik, Prajval Palwade, and Prathamesh Khondhare. "Detecting Toxic comments By Using LSTM-CNN Model." International Journal Of Advance Research And Innovative Ideas In Education 10.3(2024) : 2830-2841.
APA Bhalshankar S.T, Shubham Gupta, Akhilesh Yadav, Vaishnav Mahadik, Prajval Palwade, & Prathamesh Khondhare. (2024). Detecting Toxic comments By Using LSTM-CNN Model. International Journal Of Advance Research And Innovative Ideas In Education, 10(3), 2830-2841.
Chicago Bhalshankar S.T, Shubham Gupta, Akhilesh Yadav, Vaishnav Mahadik, Prajval Palwade, and Prathamesh Khondhare. "Detecting Toxic comments By Using LSTM-CNN Model." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 3 (2024) : 2830-2841.
Oxford Bhalshankar S.T, Shubham Gupta, Akhilesh Yadav, Vaishnav Mahadik, Prajval Palwade, and Prathamesh Khondhare. 'Detecting Toxic comments By Using LSTM-CNN Model', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 3, 2024, p. 2830-2841. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Detecting_Toxic_comments_By_Using_LSTM_CNN_Model_ijariie24077.pdf (Accessed : ).
Harvard Bhalshankar S.T, Shubham Gupta, Akhilesh Yadav, Vaishnav Mahadik, Prajval Palwade, and Prathamesh Khondhare. (2024) 'Detecting Toxic comments By Using LSTM-CNN Model', International Journal Of Advance Research And Innovative Ideas In Education, 10(3), pp. 2830-2841IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Detecting_Toxic_comments_By_Using_LSTM_CNN_Model_ijariie24077.pdf (Accessed : )
IEEE Bhalshankar S.T, Shubham Gupta, Akhilesh Yadav, Vaishnav Mahadik, Prajval Palwade, and Prathamesh Khondhare, "Detecting Toxic comments By Using LSTM-CNN Model," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 3, pp. 2830-2841, May-Jun 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/Detecting_Toxic_comments_By_Using_LSTM_CNN_Model_ijariie24077.pdf [Accessed : ].
Turabian Bhalshankar S.T, Shubham Gupta, Akhilesh Yadav, Vaishnav Mahadik, Prajval Palwade, and Prathamesh Khondhare. "Detecting Toxic comments By Using LSTM-CNN Model." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 3 ().
Vancouver Bhalshankar S.T, Shubham Gupta, Akhilesh Yadav, Vaishnav Mahadik, Prajval Palwade, and Prathamesh Khondhare. Detecting Toxic comments By Using LSTM-CNN Model. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(3) : 2830-2841. Available from: https://ijariie.com/AdminUploadPdf/Detecting_Toxic_comments_By_Using_LSTM_CNN_Model_ijariie24077.pdf
BibTex EndNote RefMan RefWorks

Number Of Downloads



Save in Google Drive

Similar-Paper

TitleArea of ResearchAuther NameAction
A Review on Ad Makers- Creation & Design Advertising CampaignsComputer EngineeringSwapnil Ohol1 Download
DETECTING FACIAL FORGERIES USING NOVEL FREQUENCY CONVOLUTIONAL NEURAL NETWORKComputer ScienceChikati Madhava Rao Download
Detecting Diabetes Early and Classifying Risk with Interpretable Deep Learning ModelsComputer ScienceMohmad Ahmed Ali Download
Enhance Accuracy of Medical Disease Prediction Using Machine Learning TechniquesMachine LearningSandeep Singh Sikarwar Download
Transformer-based Generative Adversarial Network (GAN) hybrid model for sentiment analysisInformation Technologyhridesh kumar Download
A Step Ahead in Operational Resilience - Integrating Observability with DevOpsComputer EngineeringAmit Sengupta Download
The Influence of Artificial Intelligence and Human-Computer Interaction on Enhancing Children's Learning ExperiencesComputer Engineering,Supriya S Kendhuli Download
Medicinal Plants Identification Using Deep LearningComputer Engineering Rohan Kumar Verma Download
“How to balance automation and ethics when rolling out AI automation in an Educational Framework”Computer SciecneSudhakar mishra Download
Microservices Fault Tolerance PatternsComputer EngineeringAmit Sengupta Download
Forecasting and Categorization of Cardiac Arrhythmia using Soft Computational ApproachesComputer Science and EngineeringDr. Sivaprakash. C Download
Enhancing CybersecurityComputer EngineeringG Tarun Download
Microservices Design PatternsComputer EngineeringAmit Sengupta Download
The Novel Approach to Detect Kidney Stones Using Deep Learning and Convolutional Neural NetworksCSEN Manjunatha Download
An investigation into the use of deep learning and image processing in the domain of diabetic medical careCSEHR Ravikumar Download
12