SemEval 2020 Task 9
Related Papers
CS-Embed at SemEval-2020 Task 9: The Effectiveness of Code-switched Word Embeddings for Sentiment Analysis
AbstractThe 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 …...
Deep Learning Brasil - NLP at SemEval-2020 Task 9: Sentiment Analysis of Code-Mixed Tweets Using Ensemble of Language Models
AbstractIn this paper, we describe a methodology to predict sentiment in code-mixed tweets (hindi-english). Our team called verissimo.manoel in CodaLab developed an approach based on an ensemble of four models …...
IUST at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text Using Deep Neural Networks and Linear Baselines
AbstractSentiment Analysis is a well-studied field of Natural Language Processing. However, the rapid growth of social media and noisy content within them poses significant challenges in addressing this problem with …...
LT3 at SemEval-2020 Task 9: Cross-lingual Embeddings for Sentiment Analysis of Hinglish Social Media Text
AbstractThis paper describes our contribution to the SemEval-2020 Task 9 on Sentiment Analysis for Code-mixed Social Media Text. We investigated two approaches to solve the task of Hinglish sentiment analysis. …...