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COVID-19 has become a global pandemic that has affected not only the health sector but also economic, social, and psychological well-being. Individuals are using social media platforms to communicate their feelings and sentiments about the pandemic. One of the most debated topics in that regard is the vaccine. People are divided mainly into two groups, pro-vaccine and anti-vaccine. This article aims to explore Arabic Sentiment Analysis for Vaccine-Related COVID-19 Tweets (ASAVACT) to quantify sentiment polarity shared publicly, and it is considered the first and the largest human-annotated dataset in Arabic. The analysis is done using state-of-the-art deep learning models that proved superiority in the field of language processing and analysis. The models are the stacked gated recurrent unit (SGRU), the stacked bidirectional gated recurrent unit (SBi-GRU), and the ensemble architecture of SGRU, SBi-GRU, and AraBERT. Additionally, this article presents the largest Arabic Twitter corpus on COVID-19 vaccination, with 32,476 annotated Tweets. The results show that the ensemble model outperformed other singular models with at least 7% accuracy enhancement.

In this study, imidazole was used for the first time in esterification of free fatty acid (FFA) from acidic oil and for palmitic acid imidazole production. The FFA content in jatropha oil mixed with crude palm oil (CPO) was significantly reduced from 10.57% to 1.73% under the following optimum conditions (25% imidazole dosage, 30 mins of reaction time, reaction temperature at 60 °C and methanol to oil molar ratio of 20:1). This research opens up new possibilities for utilizing imidazole as a catalyst in various esterification processes, offering a promising and eco-friendly pathway for industrial applications.