Evidence map›Paper›PMID 37256028›Full record

ArticleInternational journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management2023

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

Isha Gupta, Indranath Chatterjee, Neha Gupta

Abstract read
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Article in International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Isha GuptaFaculty of Computer Applications, Manav Rachna International Institute of Research and Studies, Faridabad, 121003 India.
Indranath ChatterjeeDepartment of Computer Engineering, Tongmyong University, Busan, 48520 South Korea.
Neha GuptaFaculty of Computer Applications, Manav Rachna International Institute of Research and Studies, Faridabad, 121003 India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Google news feedLatent Dirichlet allocationLegal documentsSentiment analysisTopic modelingValence aware dictionary sentiment reasoner

Identifiers

PMID37256028
PMCPMC10133901

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.