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The growing popularity and applications of sentiment analysis of social mediaposts has naturally led to sentiment analysis of posts written in multiplelanguages, a practice known as code-switching. While recent research intocode-switched posts has focused on the use of multilingual word embeddings,these embeddings were not trained on code-switched data. In this work, wepresent word-embeddings trained on code-switched tweets, specifically thosethat make use of Spanish and English, known as Spanglish. We explore theembedding space to discover how they capture the meanings of words in bothlanguages. We test the effectiveness of these embeddings by participating inSemEval 2020 Task 9: ~\emph{Sentiment Analysis on Code-Mixed Social MediaText}. We utilised them to train a sentiment classifier that achieves an F-1score 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 29participating teams, beating the baseline.

CS-Embed at SemEval-2020 Task 9: The effectiveness of code-switched word embeddings for sentiment analysis