Arra’Di Nur Rizal author Sara Stymne author 2020-may text English eng European Language Resources Association Marseille, France conference publication 979-10-95546-66-5 Code-mixed texts are abundant, especially in social media, and poses a problem for NLP tools, which are typically trained on monolingual corpora. In this paper, we explore and evaluate different types of word embeddings for Indonesian–English code-mixed text. We propose the use of code-mixed embeddings, i.e. embeddings trained on code-mixed text. Because large corpora of code-mixed text are required to train embeddings, we describe a method for synthesizing a code-mixed corpus, grounded in literature and a survey. Using sentiment analysis as a case study, we show that code-mixed embeddings trained on synthesized data are at least as good as cross-lingual embeddings and better than monolingual embeddings. rizal-stymne-2020-evaluating https://www.aclweb.org/anthology/2020.calcs-1.4 2020-may 26 35