Subhrajit Dey


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A Novel Approach towards Cross Lingual Sentiment Analysis using Transliteration and Character Embedding
Rajarshi Roychoudhury | Subhrajit Dey | Md Akhtar | Amitava Das | Sudip Naskar
Proceedings of the 19th International Conference on Natural Language Processing (ICON)

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.