Effective Approach to Develop a Sentiment Annotator For Legal Domain in a Low Resource Setting

Gathika Ratnayaka, Nisansa de Silva, Amal Shehan Perera, Ramesh Pathirana
arXiv preprint arXiv:2011.00318

Analyzing the sentiments of legal opinions available in Legal Opinion Texts can facilitate several use cases such as legal judgement prediction, contradictory statements identification and party-based sentiment analysis. However, the task of developing a legal domain specific sentiment annotator is challenging due to resource constraints such as lack of domain specific labelled data and domain expertise. In this study, we propose novel techniques that can be used to develop a sentiment annotator for the legal domain while minimizing the need for manual annotations of data.

Keywords: Natural Language Processing | Machine Learning / Deep Learning | Law |