The growing popularity and applications of sentiment analysis of social media
posts has naturally led to sentiment analysis of posts written in multiple
languages, a practice known as code-switching. While recent research into
code-switched posts has focused on the use of multilingual word embeddings,
these embeddings were not trained on code-switched data. In this work, we
present word-embeddings trained on code-switched tweets, specifically those
that make use of Spanish and English, known as Spanglish. We explore the
embedding space to discover how they capture the meanings of words in both
languages. We test the effectiveness of these embeddings by participating in
SemEval 2020 Task 9: ~\emph{Sentiment Analysis on Code-Mixed Social Media
Text}. We utilised them to train a sentiment classifier that achieves an F-1
score of 0.722. This is higher than the baseline for the competition of 0.656,
with our team (codalab username \emph{francesita}) ranking 14 out of 29
participating teams, beating the baseline.Comment: Accepted at SemEval-2020, COLIN
