Enhanced quantum long short-term memory neural network based multi-task learning for sentimental analysis and cyberbullying detection

Increasing usage of social media by individuals led to a significant rise in cyberbullying. Detecting sarcasm is challenging because many comments contain sarcasm or aggressive language. Text sentiment classification helps in the identification of abusive words using some beneficial features. Several machine learning algorithms are used in the detection of cyberbullying by using natural language processing mechanism. However, Deep Learning (DL) algorithms provides significant improvement in outcomes due to various reasons such as effectively segments text and image data, handling of large dataset, automatic extraction of features. Hence, a novel DL method Hybrid averaged and weighted averaged review vector Quantum long short-term memory neural based Multi-task Learning with Black-winged kite Optimization (HQMLBO) is proposed. Pre-processing is performed to clean the raw data. Next, features are extracted using hybrid multi-scale with hash vectorization, and relevant features are selected via the hybrid pine cone geyser-inspired optimization algorithm. Finally, sentiment classification and cyberbullying detection are performed using HQMLBO. Various DL methods are analysed and compared over three datasets using Python software. The proposed model outperforms existing methods in terms of accuracy of 95.68% for internet movie database, 92.5% for yelp polarity and 97.86% for cyberbullying classification dataset.