Title: Spearman chimp optimisation algorithm feature selection and fuzzy weight long-short-term memory classifier for cyberbullying Twitter data

Authors: M. Menaka; P. Sujatha

Addresses: Department of Information Technology, Vels Institute of Science, Technology and Advanced Studies, Chennai, Tamil Nadu, India ' Department of Information Technology, Vels Institute of Science, Technology and Advanced Studies, Chennai, Tamil Nadu, India

Abstract: Social connections developed within narrow cultural limits, such as physical locations, prior to the invention of information and communication technology (ICT). Social technologies have revolutionised online social networks, user-generated content, and rich human behaviour data. Online social networks (OSN) promote social interaction but also trolling, hate speech, and cyberbullying. NLP-based automatic detection is essential to ending cyberbullying. A deep learning algorithm is suggested to detect cyberbullying aggression in this work automatically. Pre-processing, feature extraction, feature selection, and classification are among the processes included in the suggested workflow. The initial pre-processing steps for the Twitter database include noise removal, tokenisation, and stemming. The features from the pre-processed database have been extracted using the SAE, TF-IDF, and other techniques. To choose the subset of characteristics, the SCOA is next applied. FWLSTM classifier is then given features. The K-nearest neighbour (KNN), ANN, random forest (RF), and EK-SVM classifiers are contrasted with the FWLSTM classifier. Results are evaluated using precision, recall (sensitivity), specificity, false positive, false discovery, miss, and accuracy.

Keywords: stacked auto-encoder; SAE; spearman chimp optimisation algorithm; SCOA; fuzzy weight long short-term memory; FWLSTM; artificial neural network; ANN; enhanced kernel with support vector machine; EK-SVM; term frequency-inverse document frequency; TF-IDF.

DOI: 10.1504/IJIEI.2025.150102

International Journal of Intelligent Engineering Informatics, 2025 Vol.13 No.4, pp.395 - 431

Received: 02 May 2024
Accepted: 14 Aug 2024
Published online: 01 Dec 2025 *

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