  Code-mixing is the practice of alternating between two or more languages.
Mostly observed in multilingual societies, its occurrence is increasing and
therefore its importance. A major part of sentiment analysis research has been
monolingual, and most of them perform poorly on code-mixed text. In this work,
we introduce methods that use different kinds of multilingual and cross-lingual
embeddings to efficiently transfer knowledge from monolingual text to
code-mixed text for sentiment analysis of code-mixed text. Our methods can
handle code-mixed text through a zero-shot learning. Our methods beat
state-of-the-art on English-Spanish code-mixed sentiment analysis by absolute
3\% F1-score. We are able to achieve 0.58 F1-score (without parallel corpus)
and 0.62 F1-score (with parallel corpus) on the same benchmark in a zero-shot
way as compared to 0.68 F1-score in supervised settings. Our code is publicly
available.
