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Sentiment Analysis of code-mixed text has diversified applications in opinionmining ranging from tagging user reviews to identifying social or politicalsentiments of a sub-population. In this paper, we present an ensemblearchitecture of convolutional neural net (CNN) and self-attention based LSTMfor sentiment analysis of code-mixed tweets. While the CNN component helps inthe classification of positive and negative tweets, the self-attention basedLSTM, helps in the classification of neutral tweets, because of its ability toidentify correct sentiment among multiple sentiment bearing units. We achievedF1 scores of 0.707 (ranked 5th) and 0.725 (ranked 13th) on Hindi-English(Hinglish) and Spanish-English (Spanglish) datasets, respectively. Thesubmissions for Hinglish and Spanglish tasks were made under the usernamesayushk and harsh_6 respectively.

BAKSA at SemEval-2020 Task 9: Bolstering CNN with Self-Attention for Sentiment Analysis of Code Mixed Text