Sentiment analysis plays an important role in obtaining speakers' opinions or feelings towards events, products, topics, or services, helping businesses to improve their products. Moreover, governments and organizations investigate and solve current social issues by analyzing perspectives and feelings. This study evaluated the habit of chewing Khat (qat) leaves among the Yemeni society. Chewing Khat plant leaves, is a common habit in Yemen and East Africa. This paper proposes a model to detect information about the Khat chewing habit, how people explore it, and the preference for Khat leaves among Arabic people. A dataset consisting of user comments on 18 youtube videos was prepared through several natural language processing techniques. Several experiments were conducted using six machine learning classifiers and four ensemble methods. Support Vector Machine and Linear Regression had almost 80% accuracy, whereas xgboot was the most accurate ensemble method reaching 77%.

The novel human Corona disease (COVID-19) is a pulmonary sickness brought on by an extraordinarily outrageous respiratory condition crown 2. (SARS -CoV-2). Chest radiography imaging has a significant role in the screening, early diagnosis, and follow-up of the suspected individuals due to the effects of COVID-19 on pneumonic-sensitive tissue. It also has a severe impact on the economy as a whole. If positive patients are identified early, the spread of the pandemic illness can be slowed. To determine whether people are at risk for illnesses, a COVID-19 infection prediction is critical. This paper categorizes chest CT samples of COVID-19 affected patients. The two-stage proposed deep learning technique produces spatial function from images, so it is a very expeditious manner for image category hassle. Extensive experiments are drawn by considering the benchmark chest-Computed Tomography (chest-CT) image datasets. Comparative evaluation reveals that our proposed method outperforms amongst other 20 different existing pre-trained models. The test outcomes constitute that our proposed model achieved the best rating of 97.6%, 0.964, 0.964, and 0.982 concerning the accuracy, precision, recall, specificity, and F1score, respectively.

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Coronavirus pandemic has created complex challenges and adverse conditions. Sentiment analysis is a process of studying the user application. Because of using the internet in daily activities, many domains and organizations concentrate on analysis or getting user feedback to take the right decision. This paper is review the existing applications that used a sentiments analysis to identify major sentiment trends associated with the push to reopen the analyzing sentiment in social media like Twitter, etc. Data time aligned to the COVID-19 reopening debate. In addition, discover the most popular techniques and approaches. This study focus the research articles in high impact journals that published during the epidemics from 2019 to 2021. The research question that this study answer it are. This study can be beneficial to many domains such as sentiment analysis, text mining, research in related areas, and postgraduate students. This research could present valuable time sensitive opportunities for governments, and the nation into a successful new normal future. Several applications have employed in several domains, including tourism, education, business and health. Health information can be disseminated by social media and misinformation can be addressed via this platform.

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