  In today's interconnected and multilingual world, code-mixing of languages on
social media is a common occurrence. While many Natural Language Processing
(NLP) tasks like sentiment analysis are mature and well designed for
monolingual text, techniques to apply these tasks to code-mixed text still
warrant exploration. This paper describes our feature engineering approach to
sentiment analysis in code-mixed social media text for SemEval-2020 Task 9:
SentiMix. We tackle this problem by leveraging a set of hand-engineered
lexical, sentiment, and metadata features to design a classifier that can
disambiguate between "positive", "negative" and "neutral" sentiment. With this
model, we are able to obtain a weighted F1 score of 0.65 for the "Hinglish"
task and 0.63 for the "Spanglish" tasks
