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

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.