Spearman Chimp Optimisation Algorithm (SCOA) Feature Selection and Fuzzy Weight Long Short-Term Memory (FWLSTM) Classifier for Cyberbullying Twitter Data
P. Sujatha, Menaka M · International Journal of Intelligent Engineering Informatics · 2024
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.