Citation: Madni, H.A.; Umer, M.; Abuzinadah, N.; Hu, Y.-C.; Saidani, O.; Alsubai, S.; Hamdi, M.; Ashraf, I. Improving Sentiment Prediction of Textual Tweets Using Feature Fusion and Deep Machine Ensemble Model. Electronics 2023, 12, 1302. https:// doi.org/10.3390/electronics12061302 Academic Editor: Alberto Fernandez Hilario Received: 11 December 2022 Revised: 28 December 2022 Accepted: 11 January 2023 Published: 9 March 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). electronics Article Improving Sentiment Prediction of Textual Tweets Using Feature Fusion and Deep Machine Ensemble Model Hamza Ahmad Madni 1, * , Muhammad Umer 2 , Nihal Abuzinadah 3 , Yu-Chen Hu 4 , Oumaima Saidani 5 , Shtwai Alsubai 6 , Monia Hamdi 7 and Imran Ashraf 8, * 1 College of Electronic and Information Engineering, Beibu Gulf University, Qinzhou 535011, China 2 Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan 3 Faculty of Computer Science and Information Technology, King Abdulaziz University, P.O. Box 80200, Jeddah 21589, Saudi Arabia 4 Department of Computer Science & Information Management, Providence University, Sector 7, Taiwan Boulevard, Shalu District, Taichung City 43301, Taiwan 5 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia 6 Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam Bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia 7 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia 8 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea * Correspondence: hamza@bbgu.edu.cn (H.A.M.); imranashraf@ynu.ac.kr (I.A.) Abstract: Widespread fear and panic has emerged about COVID-19 on social media platforms which are often supported by falsified and altered content. This mass hysteria creates public anxiety due to misinformation, misunderstandings, and ignorance of the impact of COVID-19. To assist health professionals in addressing this epidemic more appropriately at the onset, sentiment analysis can potentially help the authorities for devising appropriate strategies. This study analyzes tweets related to COVID-19 using a machine learning approach and offers a high-accuracy solution. Experiments are performed involving different machine and deep learning models along with various features such as Word2vec, term-frequency, term-frequency document frequency, and feature fusion of both feature- generating approaches. The proposed approach combines the extra tree classifier and convolutional neural network and uses feature fusion to achieve the highest accuracy score of 99%. The proposed approach obtains far better results than existing sentiment analysis approaches. Keywords: sentiment analysis; tweet classification; machine learning; COVID-19 1. Introduction The spread of novel infectious COVID-19 necessitates an appropriate definition of cases, which are important for clinical diagnosis and health care surveillance. Monitoring the number of cases over time is essential for the development of effective therapies and the rate of dissemination. The World Health Organization (WHO) proclaimed the pandemic of coronavirus on 11 March 2020 [1]. The COVID-19 epidemic has wreaked havoc on the social and economic conditions of nations all over the world. It is one of the worst pandemics the entire planet has ever experienced. This COVID-19 pandemic has had a significant impact on every human’s life [2]. The COVID-19 pandemic had a significant impact on each nation’s medical and financial position. According to WHO, this pandemic will have an impact on the healthcare systems of about 75% of the world’s nations by 2020. Beginning with a few Asian and European nations, COVID-19 will eventually extend to 220 nations worldwide by the Electronics 2023, 12, 1302. https://doi.org/10.3390/electronics12061302 https://www.mdpi.com/journal/electronics