Anxiety and depression can have a significant impact on students’ academic performance, however, these mental health impacts were increased during the Covid-19 pandemic, and accordingly students and parents need some people to share their feelings together; however, there are different types of social media apps and platforms such as Facebook, Twitter, Reddit, Instagram, and others. Twitter is one of the most popular social application that people prefer to share their emotional states. Interestingly, the psychologist and computer scientists are inspired to study these emotions. In this paper, we propose a chatbot for detecting the students feeling by using machine-learning algorithms. The authors used a dataset of tweets from Kaggle’s paltform, and it includes 41157 tweets that are all related to the COVID19. The tweets are classified into categories based on the feeling: Positive and negative. The authors applied Machine Learning algorithms, Support Vector Machines (SVM) and the Naïve Bayes (NB) and accordingly they compared the accuracy between them. In addition to that, the classifiers were evaluated and compared after changing the test split ratio. The result shows that the accuracy performance of SVM algorithm is better than Naïve Bayes algorithm, but the speed is extremely slow compared to Naive Bayes model. In future, other neural network algorithms such as the RNN, LSTM will be implemented, and Arabic tweets will be included in the future.