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

TL;DR

This paper introduces a transfer learning method to accurately identify sentiment in legal language, addressing domain-specific challenges and improving accuracy by over 6% compared to existing models.

Contribution

It presents a novel transfer learning approach tailored for legal sentiment analysis, enhancing prediction accuracy in a complex, specialized language domain.

Findings

Over 6% accuracy improvement in legal sentiment prediction

Effective transfer learning methodology for domain adaptation

Applicable to other specialized domains

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.