Detecting Toxic comments By Using LSTM-CNN Model

Bhalshankar S.T; Shubham Gupta; Akhilesh Yadav; Vaishnav Mahadik; Prajval Palwade; Prathamesh Khondhare

Detecting Toxic comments By Using LSTM-CNN Model

May 2024

Vol-10, Issue-3

Paper ID: 24077

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Computer Engineering

Keywords

Toxic Comments Convolutional Neural Network Toxic Detection Deep Learning Text Classification Machine Learning Model Evaluation Feature Extraction Data Preprocessing

Abstract

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.

License

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

Author Information

#	Name	Institute / Affiliation
1	Bhalshankar S.T	MIT College Of Railway Engineering & Research , Barshi
2	Shubham Gupta	MIT College Of Railway Engineering & Research , Barshi
3	Akhilesh Yadav	MIT College Of Railway Engineering & Research , Barshi
4	Vaishnav Mahadik	MIT College Of Railway Engineering & Research , Barshi
5	Prajval Palwade	MIT College Of Railway Engineering & Research , Barshi
6	Prathamesh Khondhare	MIT College Of Railway Engineering & Research , Barshi

How to Cite

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

APA Style

S.T, Bhalshankar, Gupta, Shubham, Yadav, Akhilesh, Mahadik, Vaishnav, Palwade, Prajval, & Khondhare, Prathamesh (2024). Detecting Toxic comments By Using LSTM-CNN Model. International Journal of Advance Research and Innovative Ideas In Education, 10(3), 2830-2841.

MLA Style

S.T, Bhalshankar, et al. "Detecting Toxic comments By Using LSTM-CNN Model." International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 3, 2024, pp. 2830-2841.

IEEE Style

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, 2024.

Vancouver Style

S.T Bhalshankar, Gupta Shubham, Yadav Akhilesh, Mahadik Vaishnav, Palwade Prajval, Khondhare Prathamesh. Detecting Toxic comments By Using LSTM-CNN Model. International Journal of Advance Research and Innovative Ideas In Education. 2024;10(3):2830-2841.

Harvard Style

S.T, Bhalshankar, Gupta, Shubham, Yadav, Akhilesh, Mahadik, Vaishnav, Palwade, Prajval, & Khondhare, Prathamesh (2024) 'Detecting Toxic comments By Using LSTM-CNN Model', International Journal of Advance Research and Innovative Ideas In Education, 10(3), pp. 2830-2841.

Chicago Style

S.T, Bhalshankar, et al. "Detecting Toxic comments By Using LSTM-CNN Model." International Journal of Advance Research and Innovative Ideas In Education 10, no. 3 (2024): 2830-2841.

Turabian Style

S.T, Bhalshankar, et al. "Detecting Toxic comments By Using LSTM-CNN Model." International Journal of Advance Research and Innovative Ideas In Education 10, no. 3 (2024): 2830-2841.

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