1. A hybrid deep learning approach for Assamese toxic comment detection in social media.
- Author
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Neog, Mandira and Baruah, Nomi
- Subjects
CONVOLUTIONAL neural networks ,DEEP learning ,SOCIAL media ,ONLINE comments - Abstract
The presence of toxic comments on online platforms creates significant barriers to encouraging positive conversation and user involvement. The present research introduces a novel hybrid deep learning methodology that combines Bidirectional long-short-term memory (BiLSTM) and Convolutional Neural Network (CNN) architectures to improve toxic comment identification. The major goal is to improve accuracy and efficiency in detecting Assamese toxic content, particularly on social media sites. Due to insufficient existing datasets, information is manually gathered from a wide range of public domains, allowing for a thorough evaluation of the performance of the hybrid method. We used two alternative activation functions in our experiments: sigmoid and softmax. The sigmoid activation obtained 88.43% accuracy, while the softmax activation outperformed with 90.51% accuracy. We have also made an effort to use our suggested Approach in Bengali and Hindi, two additional Indian languages. Due to their comparable Subject-Object-Verb (SOV) linguistic structure to Assamese, we chose these languages, and the results have been fairly encouraging. The findings from the research are extremely significant since they highlight that the hybrid deep learning approach is a promising option for effectively identifying toxic comments on social media networks. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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