Deep learning and natural language processing in computation for offensive language detection in online social networks by feature selection and ensemble classification techniques

Anand M. Sahay K.B. Ahmed M.A. Sultan D. Chandan R.R. Singh B.
17 January 2023 Elsevier B.V.

Theoretical Computer Science
2023 #943 203 - 218 pp.

Offensive communications have made their way into social media posts. Using computational algorithms to distinguish objectionable content is one of the most effective ways to deal with this problem. One of the most effective approaches to deal with this issue is to use computational methods to distinguish undesirable content. This research aims to tackle MOLD_DL (Multilingual Offensive Language Detection using deep learning) techniques and natural language processing used in feature selection and classification. Here the dataset has been collected from YouTube, Twitter and Facebook, which has been pre-processed for noise removal, filtering and removing the stop words and segmented. The feature selection has been carried out for segmented data using Fuzzy based convolutional neural network (FCNN). Then the extraction of selected features and classification has been carried out using ensemble architecture of Bi-LSTM model with Naïve Bayes architecture hybrid with Support Vector Machines (SVM). Evaluation of offensive language detection is classified automatically based on the emotions of the text. Here the experimental analysis has been carried out for YouTube, Twitter and Facebook datasets in terms of accuracy of 98%, precision of 95%, recall of 90%, F-1 score of 92.5% and RMSE of 45% with the confusion matrix in detecting offensive text of various languages.

Classification , Deep learning , Feature selection , Multilingual offensive language detection , NLP , Offensive communications

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Independent Researcher, India
Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Uttar Pradesh, Gorakhpur, Postal Pin Code-273010, India
Department of Computer Engineering, College of Computer Engineering & Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
Al-Farabi Kazakh National University, Almaty, Kazakhstan
International Information Technology University, Almaty, Kazakhstan
Computer Science & Engineering, Shambhunath Institute of Engineering & Technology, India
Mechanical Engineering, GLA University Mathura, UP, Mathura, India

Independent Researcher
Department of Electrical Engineering
Department of Computer Engineering
Al-Farabi Kazakh National University
International Information Technology University
Computer Science & Engineering
Mechanical Engineering

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