Trinity at SemEval-2023 Task 12: Sentiment Analysis for Low-resource African Languages using Twitter Dataset
Shashank Rathi, Siddhesh Pande, Harshwardhan Atkare, Rahul Tangsali, Aditya Vyawahare, Dipali Kadam
Abstract
In this paper, we have performed sentiment analysis on three African languages (Hausa, Swahili, and Yoruba). We used various deep learning and traditional models paired with a vectorizer for classification and data -preprocessing. We have also used a few data oversampling methods to handle the imbalanced text data. Thus, we could analyze the performance of those models in all the languages by using weighted and macro F1 scores as evaluation metrics.- Anthology ID:
- 2023.semeval-1.161
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1161–1165
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2023.semeval-1.161/
- DOI:
- 10.18653/v1/2023.semeval-1.161
- Cite (ACL):
- Shashank Rathi, Siddhesh Pande, Harshwardhan Atkare, Rahul Tangsali, Aditya Vyawahare, and Dipali Kadam. 2023. Trinity at SemEval-2023 Task 12: Sentiment Analysis for Low-resource African Languages using Twitter Dataset. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1161–1165, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Trinity at SemEval-2023 Task 12: Sentiment Analysis for Low-resource African Languages using Twitter Dataset (Rathi et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2023.semeval-1.161.pdf