Abstract
Code-switching refers to the textual or spoken data containing multiple languages. Application of natural language processing (NLP) tasks like sentiment analysis is a harder problem on code-switched languages due to the irregularities in the sentence structuring and ordering. This paper shows the experiment results of building a Kernel based Extreme Learning Machines(ELM) for sentiment analysis for code-switched Dravidian languages with English. Our results show that ELM performs better than traditional machine learning classifiers on various metrics as well as trains faster than deep learning models. We also show that Polynomial kernels perform better than others in the ELM architecture. We were able to achieve a median AUC of 0.79 with a polynomial kernel.- Anthology ID:
- 2022.dravidianlangtech-1.29
- Volume:
- Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- DravidianLangTech
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 184–190
- Language:
- URL:
- https://aclanthology.org/2022.dravidianlangtech-1.29
- DOI:
- 10.18653/v1/2022.dravidianlangtech-1.29
- Cite (ACL):
- Mithun Kumar S R, Lov Kumar, and Aruna Malapati. 2022. Sentiment Analysis on Code-Switched Dravidian Languages with Kernel Based Extreme Learning Machines. In Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages, pages 184–190, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Sentiment Analysis on Code-Switched Dravidian Languages with Kernel Based Extreme Learning Machines (S R et al., DravidianLangTech 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.dravidianlangtech-1.29.pdf