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

• DOI: 10.3390/electronics12194125
• Publication Date: 2023-10-03T05:48:17Z
• Authors: Ahmed S. Almasoud; Hala J. Alshahrani; Abdulkhaleq Q. A....; Nabil Sharaf Almalki; Abdelwahed Motwakel
• Keywords: Sentiment Analysis; Interpretability; Hyperparameter; Benchmark (surveying); Bag-of-words model; Representation

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