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

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.
Anthology ID:
2020.semeval-1.162
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
1221–1226
Language:
URL:
https://preview.aclanthology.org/remove-affiliations/2020.semeval-1.162/
DOI:
10.18653/v1/2020.semeval-1.162
Bibkey:
Cite (ACL):
Ayush Kumar, Harsh Agarwal, Keshav Bansal, and Ashutosh Modi. 2020. BAKSA at SemEval-2020 Task 9: Bolstering CNN with Self-Attention for Sentiment Analysis of Code Mixed Text. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1221–1226, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
BAKSA at SemEval-2020 Task 9: Bolstering CNN with Self-Attention for Sentiment Analysis of Code Mixed Text (Kumar et al., SemEval 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/remove-affiliations/2020.semeval-1.162.pdf
Code
 keshav22bansal/BAKSA_IITK
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