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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
Proceedings of the 9th workshop on computational approaches to subjectivity, sentiment and social media analysis

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.

Keywords: Natural Language Processing | Machine Learning / Deep Learning | Law | Domain Adaptation | Sentiment Analysis | 

@inproceedings{gamage2018fast,
  title={{Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning}},
  author={Gamage, Viraj and Warushavithana, Menuka and de Silva, Nisansa and Perera, Amal Shehan and Ratnayaka, Gathika and Rupasinghe, Thejan},
  booktitle={Proceedings of the 9th workshop on computational approaches to subjectivity, sentiment and social media analysis},
  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.},
  year={2018},
  misc={https://goo.gl/Gs9Sco,NLP:ML:LAW,https://i1.rgstatic.net/publication/328091570_Fast_Approach_to_Build_an_Automatic_Sentiment_Annotator_for_Legal_Domain_using_Transfer_Learning/links/5bca98a492851cae21b43f9c/largepreview.png,http://bit.ly/2ROx12G},
  pages={260-265},
  doi={10.18653/v1/W18-6238}
}

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PI: Nisansa de Silva

This project delves into the synergy between Natural Language Processing (NLP) and the legal domain, presenting novel solutions to enduring challenges.