No abstract

With the COVID-19 outbreak in 2020, information about the pandemic has been exponentially increasing and spreading across various social media platforms. People across the globe have been affected in a way or another because of different aspects such as the increase in infected cases, death rate increase, financial difficulties, social distancing, being under lockdown, quarantine measures, and working remotely. With people heavily relying on social media platforms to share information more than ever, it is important to analyze their conversations to understand people's sentiments and feelings during this time of crisis to find possible ways to cope with the pandemic. This paper presents a sentiment analysis study to analyze sentiments from Arabic tweets related to COVID-19 using multiple models. After data acquisition, text preprocessing steps are performed and Term Frequency Inverse Document Frequency (TF-IDF) is used to generate feature vectors. Experiments are then done comparing multiple classifiers: Naïve Bayes, Support Vector Machine, Logic Regression, Random Forest, and K-Nearest Neighbor. A comparison of the models' performance was carried out using multiple evaluation metrics including Precision, Accuracy, Recall and F1 Score. The best performing model achieved an accuracy of around 84%.

Knowledge management (KM) comprises of managing, planning, deploying, collecting, storing, reusing, and distributing the knowledge in organizations in an organized planned way. Applying KM in an organization aims to allow for ensuring overall success as it is a method that simplifies the entire process of dealing with knowledge. Some organizations such as software development companies suffer from many issues that obstruct the KM process. This case study performed on an Egyptian IT startup aims to examine their business process and methodologies for dealing with knowledge. The results showed that they were wasn't utilizing the knowledge they had properly and suffered from a lack in KM in almost all aspects. All problems were examined and solutions to handle them were proposed to implement efficient KM.