Will_go at SemEval-2020 Task 9: An Accurate Approach for Sentiment Analysis on Hindi-English Tweets Based on Bert and Pesudo Label Strategy

Wei Bao, Weilong Chen, Wei Bai, Yan Zhuang, Mingyuan Cheng, Xiangyu Ma

Abstract
Mixing languages are widely used in social media, especially in multilingual societies like India. Detecting the emotions contained in these languages, which is of great significance to the development of society and political trends. In this paper, we propose an ensemble of pesudo-label based Bert model and TFIDF based SGDClassifier model to identify the sentiments of Hindi-English (Hi-En) code-mixed data. The ensemble model combines the strengths of rich semantic information from the Bert model and word frequency information from the probabilistic ngram model to predict the sentiment of a given code-mixed tweet. Finally our team got an average F1 score of 0.731 on the final leaderboard,and our codalab username is will_go.
Anthology ID:
2020.semeval-1.182
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:
1348–1353
Language:
URL:
https://aclanthology.org/2020.semeval-1.182
DOI:
10.18653/v1/2020.semeval-1.182
Bibkey:
Cite (ACL):
Wei Bao, Weilong Chen, Wei Bai, Yan Zhuang, Mingyuan Cheng, and Xiangyu Ma. 2020. Will_go at SemEval-2020 Task 9: An Accurate Approach for Sentiment Analysis on Hindi-English Tweets Based on Bert and Pesudo Label Strategy. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1348–1353, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
Will_go at SemEval-2020 Task 9: An Accurate Approach for Sentiment Analysis on Hindi-English Tweets Based on Bert and Pesudo Label Strategy (Bao et al., SemEval 2020)
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