CS-Embed at SemEval-2020 Task 9: The effectiveness of code-switched word embeddings for sentiment analysis
This addresses sentiment analysis for multilingual social media users, but is incremental as it applies existing embedding methods to a new data type.
The paper tackled sentiment analysis of code-switched social media text by training word embeddings on Spanish-English code-switched tweets, achieving an F-1 score of 0.722 in SemEval 2020 Task 9, which beat the baseline of 0.656 and ranked 14th out of 29 teams.
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.