Sentiment Analysis: Creating dictionary from dataset I've collected around half a million of unmarked comments from a newspage. The newspage has anti-foreign background. Because of that, a relativley  high number of comments contain hate. 

Do you have an idea how to start creating a dictionary out of the comments? The dictionary shall contain words, which are connected to hate. With the dictionary I want to perform a text analysis for hate, later.

Because there is no such dictionary for German language I tried some things. For instance, I checked how often words, which are connected to emotions, appear in the comments. But it turned up, that they seem to be contained as often as any other word. I also thought about a Bag-of-Words and some other things, but I don't know.

I'm doing the analysis with Python 3 and R.

Kind regards and thanks in advance!
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.