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Sentiment Analysis for COVID-19 Vaccine Popularity
( Muhammad Saeed ),( Naeem Ahmed ),( Abid Mehmood ),( Muhammad Aftab ),( Rashid Amin ),( Shahid Kamal ) 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.5
Social media is used for various purposes including entertainment, communication, information search, and voicing their thoughts and concerns about a service, product, or issue. The social media data can be used for information mining and getting insights from it. The World Health Organization has listed COVID-19 as a global epidemic since 2020. People from every aspect of life as well as the entire health system have been severely impacted by this pandemic. Even now, after almost three years of the pandemic declaration, the fear caused by the COVID-19 virus leading to higher depression, stress, and anxiety levels has not been fully overcome. This has also triggered numerous kinds of discussions covering various aspects of the pandemic on the social media platforms. Among these aspects is the part focused on vaccines developed by different countries, their features and the advantages and disadvantages associated with each vaccine. Social media users often share their thoughts about vaccinations and vaccines. This data can be used to determine the popularity levels of vaccines, which can provide the producers with some insight for future decision making about their product. In this article, we used Twitter data for the vaccine popularity detection. We gathered data by scraping tweets about various vaccines from different countries. After that, various machine learning and deep learning models, i.e., naive bayes, decision tree, support vector machines, k-nearest neighbor, and deep neural network are used for sentiment analysis to determine the popularity of each vaccine. The results of experiments show that the proposed deep neural network model outperforms the other models by achieving 97.87% accuracy.
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Arshad Ifzan,Qureshi Khizar,Lee Shern-Long,Khan Safia,Abid Muhammad Amin,Bokhari Awais,Bahajjaj Aboud Ahmed Awadh,Ahmed Muhammad Naeem 한국화학공학회 2023 Korean Journal of Chemical Engineering Vol.40 No.10
In the current study, the N,N′,N″-(1,3,5-triazine-2,4,6-triyl)tris(1-phenylmethanimine) (MBSB) condensation product of melamine (triazine) and benzaldehyde was investigated as a mild steel corrosion inhibitor in a 0.5 M HCl. The ability of the synthesized tris-Schiff base to suppress corrosion was evaluated utilizing weight loss measurements and electrochemical techniques. The maximum inhibition efficiency of 94.78%, 93.99% and 93.80% was achieved using 100 ppm of MBSB in weight loss measurements, polarization, and EIS tests, respectively. It was observed that increasing inhibitor concentration enhanced inhibition performance, whereas increasing temperature lowered inhibition performance. The analyses demonstrated that the synthesized tris-Schiff base inhibitor followed the Langmuir adsorption isotherm, and the inhibitor was an effective mixed-type inhibitor having a low cathodic predominance. According to the electrochemical impedance measurements, the Rct values increased with the increase of inhibitor concentration. In addition, theoretical calculations using density functional theory (DFT) were performed to reveal the anticorrosion mechanism. The weight loss and electrochemical assessments were also supported by surface characterization analysis and show a substantial smoothness in the surface morphology.
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