Universiti Teknologi Malaysia Institutional Repository

An integrated text-based hybrid RNN-CNN model for toxic comment classification.

Wee, Tan Chi and Kit, Chaw Jun and Yiqi, Tew and Lang, Wong Siaw and Tan, Gloria Jennis and Mohd. Suaib, Norhaida (2023) An integrated text-based hybrid RNN-CNN model for toxic comment classification. In: 4th Tarumanagara International Conference of the Applications of Technology and Engineering, TICATE 2021, 5 August 2021 - 6 August 2021, Jakarta, Indonesia - virtual, Online.

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Official URL: http://dx.doi.org/10.1063/5.0125995

Abstract

Forum and social media especially Facebook, Instagram and Twitter play a vital role in the communication and become the pivotal role in expressing feelings and opinions toward certain posts and news through leaving a comment and review. The comment and review can be positive, neutral, or negative. There have been several longitudinal studies involving online comments showed that one of five comments, 20 percent of comments are toxic comments. Toxic comments are those negative comments that can greatly affect a person emotionally which might cause people to suffer from mental problems. Hence, toxic comments detection is fast becoming a key instrument in protecting social media platforms' users from cyberbullying. This paper seeks to remedy this issue by proposing and comparing different artificial intelligence models to classify the toxic online comments. Thus, six different models with various algorithms which are CNN, RNN, Hybrid-NN, Linear SVC, multinomial naive bayes and logistic regression will be trained with a pre-processed dataset and evaluated with cross validation and ROC AUC. These models are then compared by observing the accuracy of each model and result shows that Hybrid-Neural network, combination between RNN and CNN score the top of the chart with accuracy of 0.9778 which suggest the best model to classify the online comments.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Artificial intelligence, Artificial neural networks, News and events, Review, Regression analysis.
Subjects:T Technology > T Technology (General) > T58.6-58.62 Management information systems
Divisions:Computer Science and Information System
ID Code:107363
Deposited By: Muhamad Idham Sulong
Deposited On:03 Sep 2024 06:26
Last Modified:03 Sep 2024 06:26

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