Metaphors are a common facet of written and spoken language. For humans, it is pretty easy to identify and interpret metaphors, but machines struggle to match this capability. Much research about metaphors has been done in the last decades, but mainly for English using different approaches - ranging from rule-based to deep learning-based systems. As of the date of this thesis, there has been no research done for computational metaphor processing for the Estonian language. In this thesis, the research in the field of computational metaphors is explicitly applied to the Estonian language. All the methods implemented are unsupervised or semisupervised because the resources for Estonian regarding metaphors do not exist. This thesis also attempts to incorporate contextualized embeddings from the BERT language model into metaphor identification systems to enhance performance. For testing the performance of the methods, a new evaluation dataset for the Estonian language was created1. This dataset contains 500 sentences, from which 232 sentences contain VERB-NOUN phrase where VERB is used metaphorically and 268 which the VERB was used literally. The best results were obtained using BERT embeddings alongside with information from Estonian WordNet.