BAKSA at SemEval-2020 Task 9: Bolstering CNN with Self-Attention for Sentiment Analysis of Code Mixed Text
Ayush Kumar, Harsh Agarwal, Keshav Bansal, Ashutosh Modi · 2020
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 5 th ) and 0.725 (ranked 13 th ) 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.