IIITG-ADBU at SemEval-2020 Task 9: SVM for Sentiment Analysis of English-Hindi Code-Mixed Text

Arup Baruah, Kaushik Das, Ferdous Barbhuiya, Kuntal Dey


Abstract
In this paper, we present the results that the team IIITG-ADBU (codalab username ‘abaruah’) obtained in the SentiMix task (Task 9) of the International Workshop on Semantic Evaluation 2020 (SemEval 2020). This task required the detection of sentiment in code-mixed Hindi-English tweets. Broadly, we performed two sets of experiments for this task. The first experiment was performed using the multilingual BERT classifier and the second set of experiments was performed using SVM classifiers. The character-based SVM classifier obtained the best F1 score of 0.678 in the test set with a rank of 21 among 62 participants. The performance of the multilingual BERT classifier was quite comparable with the SVM classifier on the development set. However, on the test set it obtained an F1 score of 0.342.
Anthology ID:
2020.semeval-1.121
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venues:
*SEMEVAL | COLING
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
946–950
Language:
URL:
https://www.aclweb.org/anthology/2020.semeval-1.121
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.semeval-1.121.pdf