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

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.

Enhanced Deep Learning-Based Model for Sentiment Analysis to Identify Sarcasm Appeared in the News

“…In written text, there are no facial cues or vocal tones for a better understanding of the text. Sarcasm detection is a specific area of sentiment analysis [1,2] which is again a sub-field of natural language programming (NLP) [3] study. Here, the goal is to identify the sarcasm rather than to determine if the emotion is positive or negative.…”