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

arXiv: https://arxiv.org/abs/2011.00318 · HTML: https://mlsanity.com/paper/2011.00318/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Some Languages are More Equal than Others: Probing Deeper into the Linguistic Disparity in the NLP World](https://mlsanity.com/paper/2210.08523/) (2022)
- [Billions of Parameters Are Worth More Than In-domain Training Data: A case study in the Legal Case Entailment Task](https://mlsanity.com/paper/2205.15172/) (2022)

## Abstract (arXiv)

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.