This Master Thesis research investigates the possibility of predicting markets fluctuations during the first months of the COVID-19 pandemic in order to enhance the returns of a portfolio constituted of sectorial ETFs. As previously researched by many authors and argued by Gu and Kurov (2020), investors rely on social media sentiment and information to make investment decisions. Over the years, sentiment analysis of social media posts for stock market prediction has attracted a lot of interest and this was demonstrated by many papers researching the topic. As argued and demonstrated by Bollen et al. (2011) in “Twitter mood predicts the stock market”, a pivotal paper in this field of research, it is possible to predict stock market fluctuations by analysing the sentiment on Twitter.