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

• URL: http://arxiv.org/abs/2007.10819v1
• Date: Tue, 21 Jul 2020 14:05:51 GMT
• Title: BAKSA at SemEval-2020 Task 9: Bolstering CNN with Self-Attention for Sentiment Analysis of Code Mixed Text
• Authors: Ayush Kumar, Harsh Agarwal, Keshav Bansal, Ashutosh Modi
• Abstract summary: We present an ensemble architecture of convolutional neural net (CNN) and self-attention based LSTM for sentiment analysis of code-mixed tweets. We achieved F1 scores of 0.707 and 0.725 on Hindi-English (Hinglish) and Spanish-English (Spanglish) datasets, respectively.
• Score: 4.456122555367167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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