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Sentiment Analysis is a well-studied field of Natural Language Processing.However, the rapid growth of social media and noisy content within them posessignificant challenges in addressing this problem with well-established methodsand tools. One of these challenges is code-mixing, which means using differentlanguages to convey thoughts in social media texts. Our group, with the name ofIUST(username: TAHA), participated at the SemEval-2020 shared task 9 onSentiment Analysis for Code-Mixed Social Media Text, and we have attempted todevelop a system to predict the sentiment of a given code-mixed tweet. We useddifferent preprocessing techniques and proposed to use different methods thatvary from NBSVM to more complicated deep neural network models. Our bestperforming method obtains an F1 score of 0.751 for the Spanish-English sub-taskand 0.706 over the Hindi-English sub-task.

IUST at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text using Deep Neural Networks and Linear Baselines