We present the results of an evaluation study in the context of lexicon-based sentiment analysis resources for German texts. We have set up a comprehensive compilation of 19 sentiment lexicon resources and 20 sentiment-annotated corpora available for German across multiple domains. In addition to the evaluation of the sentiment lexicons we also investigate the influence of the following preprocessing steps and modifiers: stemming and lemmatization, part-of-speech-tagging, usage of emoticons, stop words removal, usage of valence shifters, intensifiers, and diminishers. We report the best performing lexicons as well as the influence of preprocessing steps and other modifications on average performance across all corpora. We show that larger lexicons with continuous values like SentiWS and SentiMerge perform best across the domains. The best performing configuration of lexicon and modifications considering the f1-value and accuracy averages across all corpora achieves around 67%. Preprocessing, especially stemming or lemmatization increases the performance consistently on average around 6% and for certain lexicons and configurations up to 16.5% while methods like the usage of valence shifters, intensifiers or diminishers rarely influence overall performance. We discuss domain-specific differences and give recommendations for the selection of lexicons, preprocessing and modifications.