C1 at SemEval-2020 Task 9: SentiMix: Sentiment Analysis for Code-Mixed Social Media Text using Feature Engineering

TL;DR

This paper presents a feature engineering-based sentiment analysis method for code-mixed social media text, achieving competitive F1 scores on Hinglish and Spanglish datasets in SemEval-2020.

Contribution

It introduces a novel feature engineering approach specifically designed for code-mixed sentiment analysis, addressing a gap in multilingual NLP tasks.

Findings

Achieved weighted F1 scores of 0.65 on Hinglish and 0.63 on Spanglish datasets.

Demonstrated effectiveness of lexical, sentiment, and metadata features for code-mixed sentiment classification.

Showed that feature engineering can improve sentiment analysis in multilingual social media contexts.

Abstract

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