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In this thesis, Twitter data is used to predict the intraday stock return for Tesla, Inc. We present two different methods to extract the tweets’ sentiment: A dictionary-based approach (VADER) and a machine learning approach (SVM). Additionally, we control for other dimensions as the user and discussion dimension. Then a Granger causality test and a lasso regression are conducted on a one- and fiv
