A Novel Approach towards Cross Lingual Sentiment Analysis using Transliteration and Character Embedding
Rajarshi Roychoudhury, Subhrajit Dey, Md Akhtar, Amitava Das, Sudip Naskar
Abstract
Sentiment analysis with deep learning in resource-constrained languages is a challenging task. In this paper, we introduce a novel approach for sentiment analysis in resource-constrained scenarios using character embedding and cross-lingual sentiment analysis with transliteration. We use this method to introduce the novel task of inducing sentiment polarity of words and sentences and aspect term sentiment analysis in the no-resource scenario. We formulate this task by taking a metalingual approach whereby we transliterate data from closely related languages and transform it into a meta language. We also demonstrated the efficacy of using character-level embedding for sentence representation. We experimented with 4 Indian languages – Bengali, Hindi, Tamil, and Telugu, and obtained encouraging results. We also presented new state-of-the-art results on the Hindi sentiment analysis dataset leveraging our metalingual character embeddings.- Anthology ID:
- 2022.icon-main.32
- Volume:
- Proceedings of the 19th International Conference on Natural Language Processing (ICON)
- Month:
- December
- Year:
- 2022
- Address:
- New Delhi, India
- Editors:
- Md. Shad Akhtar, Tanmoy Chakraborty
- Venue:
- ICON
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 260–268
- Language:
- URL:
- https://aclanthology.org/2022.icon-main.32
- DOI:
- Cite (ACL):
- Rajarshi Roychoudhury, Subhrajit Dey, Md Akhtar, Amitava Das, and Sudip Naskar. 2022. A Novel Approach towards Cross Lingual Sentiment Analysis using Transliteration and Character Embedding. In Proceedings of the 19th International Conference on Natural Language Processing (ICON), pages 260–268, New Delhi, India. Association for Computational Linguistics.
- Cite (Informal):
- A Novel Approach towards Cross Lingual Sentiment Analysis using Transliteration and Character Embedding (Roychoudhury et al., ICON 2022)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/2022.icon-main.32.pdf