How can a module for sentiment analysis be designed to classify tweets about covid19

Sammanfattning: The sentiment analysis of a text is getting more focus nowadays from different entities for a variety of reasons. Emotions mining (sentiment analysis) is a very interesting subject to explore thus the research question is How can a module for sentiment analysis be designed to classify tweets about Covid-19. The dataset used for this project was taken from Kaggle and preprocessed with various methods such as Bag of Words and term frequency-inverse document frequency. The models are based on the following algorithms: KNN, SVM, DT, and NB. Some models are also based on the combination of ML and Lexicon. The outcome of the experiment showed that the lexicon method with an accuracy of 87% exceeded the machine learning methods implemented in this thesis and the experiments done by the ML community in Kaggle. This implies that the traditional lexicon approach is still considered a fit choice in the sentiment analysis field.