Sentiment Analysis: Creating dictionary from dataset
When looking on the probability of word occurrence, you will get stop words and other popular words. You are interested in words that appear more in the comments (assumed hate related) than in normal use.

Get a neutral  resource (e.eg., a German newspaper, the German wikipedia, maybe Google ngrams for German). Compute the probability on the neutral source $P_{neutral}(word)$, the probability of the comments $P_{comments}(word)$ and look for the words of lift  $\frac{P_{comments}(word)}{P_{neutral}(word)} &gt; 1$. These are the words that are more popular at the comments.

As @chi wrote, many repositories can both give you a head start and help you tune the needed lift threshold (you might want words that appear much more often in the comments).

After this phase you might need do do a finer analysis. For example, I guess that there will be politicians names that will appear more often in the comments. See here for a possible approach.