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Latent Semantic Analysis based Real-world Application of Topic Modeling: A Review Study - 2022

Latent Semantic Analysis based Real-world Application of Topic Modeling: A Review Study

Survey paper on Latent Semantic Analysis based Real-world Application of Topic Modeling

Research Area:  Machine Learning

Abstract:

Today, we are living in the digital world. Much information is available on the internet about| every topic. But merely the availability of information is not enough. There is a need for automated tools that can extract hidden patterns or topics from a large number of files or data. Topic modeling is one tool available in data mining that plays a significant role in finding those unknown topics. The paper’s objective is to study the importance of topic modeling and discuss the various works done in this field. The author concludes the various fields where more work is required.

Keywords:  
Topic Modeling
Latent Semantic Analysis(LSA)
Latent Dirichlet Allocation (LDA)
Legal Documents
Machine Learning

Author(s) Name:   Isha Gupta; Indranath Chatterjee; Neha Gupta

Journal name:  

Conferrence name:  2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS)

Publisher name:  IEEE

DOI:  10.1109/ICAIS53314.2022.9742848

Volume Information: