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Social media like Twitter have become very popular in recent decades. Hashtags are new kind of metadata which make non-structured tweets into searchable semistructured content. There are varied previous methods which recommend hashtags for new tweets. However, to the best of our knowledge, there is no previous word that uses BERT embedding for this purpose. In this paper, we propose a new method called EmHash that uses neural network based on BERT embedding to recommend new hashtags for each tweet. Unlike other word embeddings, BERT embedding constructs different vectors for the same word in different contexts. Emhash succeeded in outperforming three methods LDA, SVM, and TTM with respect to recall measure.