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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.Keywords: artificial intelligence; COVID-19; emotions; Twitter data; sentiment analysis

AQILA is a beautiful serif font by Vroz Studio. AQILA will perfect for many projects: fashion, magazines, logo, branding, photography, invitations, wedding invitation, quotes, blog header, poster, advertisements, postcards, books, websites, titles, etc.

Download Aquila Font

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Articles submitted for peer review should be between 3,000 and 6,000 words (depending on article type) and follow the Chicago Manual of Style (17th edition), notes and bibliography format. Manuscripts should be submitted as a Word document. Text, including block quotations, should be 12 pt Times New Roman font and double-spaced. Figures and tables are acceptable and should be embedded in the text with short, descriptive captions.

Paper should have Type 1 fonts (scalable), not Type 3 (bit-mapped). All fonts must be embedded within the PDF file. Remember to check that all fonts are embedded and included in the figure correctly (Tip: try to avoid non-standard font sets in your figures). e24fc04721