Abstract
Code-mixing is a frequently observed phenomenon in multilingual communities where a speaker uses multiple languages in an utterance or sentence. Code-mixed texts are abundant, especially in social media, and pose a problem for NLP tools as they are typically trained on monolingual corpora. Recently, finding the sentiment from code-mixed text has been attempted by some researchers in SentiMix SemEval 2020 and Dravidian-CodeMix FIRE 2020 shared tasks. Mostly, the attempts include traditional methods, long short term memory, convolutional neural networks, and transformer models for code-mixed sentiment analysis (CMSA). However, no study has explored graph convolutional neural networks on CMSA. In this paper, we propose the graph convolutional networks (GCN) for sentiment analysis on code-mixed text. We have used the datasets from the Dravidian-CodeMix FIRE 2020. Our experimental results on multiple CMSA datasets demonstrate that the GCN with multi-headed attention model has shown an improvement in classification metrics.- Anthology ID:
- 2021.dravidianlangtech-1.8
- Volume:
- Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages
- Month:
- April
- Year:
- 2021
- Address:
- Kyiv
- Editors:
- Bharathi Raja Chakravarthi, Ruba Priyadharshini, Anand Kumar M, Parameswari Krishnamurthy, Elizabeth Sherly
- Venue:
- DravidianLangTech
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 65–72
- Language:
- URL:
- https://aclanthology.org/2021.dravidianlangtech-1.8
- DOI:
- Cite (ACL):
- Suman Dowlagar and Radhika Mamidi. 2021. Graph Convolutional Networks with Multi-headed Attention for Code-Mixed Sentiment Analysis. In Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages, pages 65–72, Kyiv. Association for Computational Linguistics.
- Cite (Informal):
- Graph Convolutional Networks with Multi-headed Attention for Code-Mixed Sentiment Analysis (Dowlagar & Mamidi, DravidianLangTech 2021)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2021.dravidianlangtech-1.8.pdf
- Data
- SentiMix