Modified Aquila Optimizer with Stacked Deep Learning-Based Sentiment Analysis of COVID-19 Tweets

Abstract

In recent times, global cities have been transforming from traditional cities to sustainable smart cities. In text sentiment analysis (SA), many people face critical issues namely urban traffic management, urban living quality, urban information security, urban energy usage, urban safety, etc. Artificial intelligence (AI)-based applications play important roles in dealing with these crucial challenges in text SA. In such scenarios, the classification of COVID-19-related tweets for text SA includes using natural language processing (NLP) and machine learning methodologies to classify tweet datasets based on their content. This assists in disseminating relevant information, understanding public sentiment, and promoting sustainable practices in urban areas during this pandemic. This article introduces a modified aquila optimizer with a stacked deep learning-based COVID-19 tweet Classification (MAOSDL-TC) technique for text SA. The presented MAOSDL-TC technique incorporates FastText, an effective and powerful text representation approach used for the generation of word embeddings. Furthermore, the MAOSDL-TC technique utilizes an attention-based stacked bidirectional long short-term memory (ASBiLSTM) model for the classification of sentiments that exist in tweets. To improve the detection results of the ASBiLSTM model, the MAO algorithm is applied for the hyperparameter tuning process. The presented MAOSDL-TC technique is validated on the benchmark tweets dataset. The experimental outcomes implied the promising results of the MAOSDL-TC technique compared to recent models in terms of different measures. This MAOSDL-TC technique improves accuracy and interpretability of sentiment prediction.