  Sentiment Analysis of code-mixed text has diversified applications in opinion
mining ranging from tagging user reviews to identifying social or political
sentiments of a sub-population. In this paper, we present an ensemble
architecture of convolutional neural net (CNN) and self-attention based LSTM
for sentiment analysis of code-mixed tweets. While the CNN component helps in
the classification of positive and negative tweets, the self-attention based
LSTM, helps in the classification of neutral tweets, because of its ability to
identify correct sentiment among multiple sentiment bearing units. We achieved
F1 scores of 0.707 (ranked 5th) and 0.725 (ranked 13th) on Hindi-English
(Hinglish) and Spanish-English (Spanglish) datasets, respectively. The
submissions for Hinglish and Spanglish tasks were made under the usernames
ayushk and harsh_6 respectively.
