Abstract
In this paper, we provide a solution to multilingual sentiment classification using deep learning. Given input text in a language, we use word translation into English and then the embeddings of these English words to train a classifier. This projection into the English space plus word embeddings gives a simple and uniform framework for multilingual sentiment analysis. A novel idea is augmentation of the training data with polar words, appearing in these sentences, along with their polarities. This approach leads to a performance gain of 7-10% over traditional classifiers on many languages, irrespective of text genre, despite the scarcity of resources in most languages.- Anthology ID:
- C16-1287
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 3053–3062
- Language:
- URL:
- https://aclanthology.org/C16-1287
- DOI:
- Cite (ACL):
- Prerana Singhal and Pushpak Bhattacharyya. 2016. Borrow a Little from your Rich Cousin: Using Embeddings and Polarities of English Words for Multilingual Sentiment Classification. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3053–3062, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Borrow a Little from your Rich Cousin: Using Embeddings and Polarities of English Words for Multilingual Sentiment Classification (Singhal & Bhattacharyya, COLING 2016)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/C16-1287.pdf