  In this paper, we present the results of the SemEval-2020 Task 9 on Sentiment
Analysis of Code-Mixed Tweets (SentiMix 2020). We also release and describe our
Hinglish (Hindi-English) and Spanglish (Spanish-English) corpora annotated with
word-level language identification and sentence-level sentiment labels. These
corpora are comprised of 20K and 19K examples, respectively. The sentiment
labels are - Positive, Negative, and Neutral. SentiMix attracted 89 submissions
in total including 61 teams that participated in the Hinglish contest and 28
submitted systems to the Spanglish competition. The best performance achieved
was 75.0% F1 score for Hinglish and 80.6% F1 for Spanglish. We observe that
BERT-like models and ensemble methods are the most common and successful
approaches among the participants.
