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IRLab_DAIICT at SemEval-2020 Task 9: Machine Learning and Deep Learning Methods for Sentiment Analysis of Code-Mixed Tweets

Apurva Parikh, Abhimanyu Singh Bisht, Prasenjit Majumder


Abstract
The paper describes systems that our team IRLab_DAIICT employed for the shared task Sentiment Analysis for Code-Mixed Social Media Text in SemEval 2020. We conducted our experiments on a Hindi-English CodeMixed Tweet dataset which was annotated with sentiment labels. F1-score was the official evaluation metric and our best approach, an ensemble of Logistic Regression, Random Forest and BERT, achieved an F1-score of 0.693.
Anthology ID:
2020.semeval-1.169
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:
1265–1269
Language:
URL:
https://aclanthology.org/2020.semeval-1.169
DOI:
10.18653/v1/2020.semeval-1.169
Bibkey:
Cite (ACL):
Apurva Parikh, Abhimanyu Singh Bisht, and Prasenjit Majumder. 2020. IRLab_DAIICT at SemEval-2020 Task 9: Machine Learning and Deep Learning Methods for Sentiment Analysis of Code-Mixed Tweets. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1265–1269, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
IRLab_DAIICT at SemEval-2020 Task 9: Machine Learning and Deep Learning Methods for Sentiment Analysis of Code-Mixed Tweets (Parikh et al., SemEval 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.169.pdf