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arXiv · 1810.01912

Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning

Viraj Gamage·Menuka Warushavithana·Nisansa de Silva·Amal Shehan Perera·Gathika Ratnayaka·Thejan Rupasinghe
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Abstract

This study proposes a novel way of identifying the sentiment of the phrases used in the legal domain. The added complexity of the language used in law, and the inability of the existing systems to accurately predict the sentiments of words in law are the main motivations behind this study. This is a transfer learning approach, which can be used for other domain adaptation tasks as well. The proposed methodology achieves an improvement of over 6\% compared to the source model's accuracy in the legal domain.

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Viraj Gamage, Menuka Warushavithana, Nisansa de Silva, Amal Shehan Perera, Gathika Ratnayaka, Thejan Rupasinghe. 2018-10-03. Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning. https://arxiv.org/abs/1810.01912

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