Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning
This addresses the problem of inaccurate sentiment prediction in legal texts for legal professionals, but it is incremental as it builds on existing transfer learning methods.
The study tackled sentiment analysis in the legal domain by proposing a transfer learning approach, achieving over 6% accuracy improvement compared to the source model.
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.