  The rapid production of data on the internet and the need to understand how
users are feeling from a business and research perspective has prompted the
creation of numerous automatic monolingual sentiment detection systems. More
recently however, due to the unstructured nature of data on social media, we
are observing more instances of multilingual and code-mixed texts. This
development in content type has created a new demand for code-mixed sentiment
analysis systems. In this study we collect, label and thus create a dataset of
Persian-English code-mixed tweets. We then proceed to introduce a model which
uses BERT pretrained embeddings as well as translation models to automatically
learn the polarity scores of these Tweets. Our model outperforms the baseline
models that use Na\"ive Bayes and Random Forest methods.
