C1 at SemEval-2020 Task 9: SentiMix: Sentiment Analysis for Code-Mixed Social Media Text using Feature Engineering
This addresses sentiment analysis for multilingual social media users, but it is incremental as it applies existing feature engineering methods to a new dataset.
The paper tackled sentiment analysis for code-mixed social media text by developing a feature engineering approach, achieving weighted F1 scores of 0.65 for Hinglish and 0.63 for Spanglish tasks.
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