A two-staged NLP-based framework for assessing the sentiments on Indian supreme court judgments

7Citations
Citations of this article
25Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Topic modeling is a powerful technique for uncovering hidden patterns in large documents. It can identify themes that are highly connected and lead to a certain region while accounting for temporal and spatial complexity. In addition, sentiment analysis can determine the sentiments of media articles on various issues. This study proposes a two-stage natural language processing-based model that utilizes Latent Dirichlet Allocation to identify critical topics related to each type of legal case or judgment and the Valence Aware Dictionary Sentiment Reasoner algorithm to assess people's sentiments on those topics. By applying these strategies, this research aims to influence public perception of controversial legal issues. This study is the first of its kind to use topic modeling and sentiment analysis on Indian legal documents and paves the way for a better understanding of legal documents.

Cite

CITATION STYLE

APA

Gupta, I., Chatterjee, I., & Gupta, N. (2023). A two-staged NLP-based framework for assessing the sentiments on Indian supreme court judgments. International Journal of Information Technology (Singapore), 15(4), 2273–2282. https://doi.org/10.1007/s41870-023-01273-z

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free