Detecting Semantic Shifts in Slovene Twitterese

This paper presents first results of automatic semantic shift detection in Slovene tweets. We use word embeddings to compare the semantic behaviour of common words frequently occurring in a reference corpus of Slovene with their behaviour on Twitter. Words with the highest model distance between the corpora are considered as semantic shift candidates. They are manually analysed and classified in order to evaluate the proposed approach as well as to gain a better qualitative understanding of the nature of the problem. Apart from the noise due to preprocessing errors (45%), the approach yields a lot of valuable candidates, especially the novel senses occurring due to daily events and the ones produced in informal communication settings.