We present the results of a project performing sentiment analysis on tweets from German politicians and party accounts for the 2021 German federal election. We collected over 58,000 tweets from the Twitter accounts of the seven parties represented in the German Bundestag, of which a selection of 2,000 tweets were annotated by three annotators. Based on the annotated data, we implemented multiple sentiment analysis approaches and evaluated the sentiment classification performance. We found that transformer-based models like bidirectional encoder from transformers (BERT) performed better than traditional machine learning models such as Naive Bayes and lexiconbased models like GerVADER. The best performing BERT model achieved an accuracy of 93.3% and macro f1 score of 93.4%. Applying sentiment analysis on the overall corpus via this method showed that overall, negative sentiment was most frequent and that there were multiple major shifts in sentiment a few months before and after the election. Furthermore, we found that tweets from opposition parties had on average more negative sentiment than those from governing parties.