English Offensive Text Detection using CNN based Bi-GRU model
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Institute of Electrical and Electronics Engineers Inc.
Abstract
Over the years, the number of users of social media has increased drastically. People frequently share their thoughts through social platforms, and this leads to an increase in hate content. In this virtual community, individuals share their views, express their feelings, and post photos, videos, blogs, and more. Social networking sites like Facebook and Twitter provide platforms to share vast amounts of content with a single click. However, these platforms do not impose restrictions on the uploaded content, which may include abusive language and explicit images unsuitable for social media. To resolve this issue, a new idea must be implemented to divide the inappropriate content. Numerous studies have been done to automate the process. In this paper, we propose a new Bi-GRU-CNN model to classify whether the text is offensive or not. The combination of the Bi-GRU and CNN models outperforms the existing models.
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Keywords
Convolutional Neural Network, Offensive Text, Social Media, Twitter, Social Networking Sites, Convolutional Neural Network Model, Virtual Communities, Inappropriate Content, Logistic Regression, Neural Network, Machine Learning, Deep Learning, Learning Models, Support Vector Machine, Mobile App, Supervised Learning, Machine Learning Models, Machine Learning Techniques, Sexual Orientation, Hate Speech, Social Media Platforms, Speech Detection, Word Embedding, Natural Language Processing Techniques, Graphics Processing Unit, Common Words, Text Data, Dense Layer
Citation
Roy, T., Islam, M. R., Miazee, A. A., Antara, A., Amin, A., & Hossain, S. (2024, October). English offensive text detection using CNN based Bi-GRU model. In 2024 2nd International Conference on Information and Communication Technology (ICICT) (pp. 1-5). IEEE.