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)
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2020.semeval-1.169.pdf