In this paper we studied which stereotypes language models learn about a wide range of social groups (e.g. asians, homosexuals, white men etc.). To get an overview of the stereotypes that exist within human populations, we first created a stereotype dataset based on search engine autocompletions. We then tested how many of the human stereotypes in our dataset were also present in language models. By using emotion lexicons that map words to the underlying emotions that they reflect (e.g., anger, fear or trust), we were able to more generally study how negatively or positively a language model is overall biased towards each group.  Our results show how attitudes towards social groups vary across models and how quickly emotions and stereotypes about a group can change when the language model is exposed to new linguistic experience.