New Frontiers in Communication and Intelligent Systems
A Case-Study on Topic Modeling Approach with Latent Dirichlet Allocation (LDA) Model
Authors: Abisheka Pon, C. Deisy and P. Sharmila
Publishing Date: 30-04-2022
ISBN: 978-81-95502-00-4
Abstract
In natural language processing, subject displaying is a sort of factual information models for identifying the points from an enormous assortment of corpus of records. Subject demonstrating is a sort of text-digging device for revelation of stowed away semantic designs in a text body. In the proposed model high layered text informational index named article.csv is handled to acquire primary themes or as often as possible happening subjects for our text information by giving the catchphrases of every point. In this work, a dataset of abstracts are collected from two different domain journals for tagging journal abstracts. The document models are built using Latent Dirichlet Allocation (LDA).Topics thus extracted can be used to get meaningful insights from the text data. In this paper LDA model is used to gives an extra analytical boost for the model. First the data is preprocessed as text data before giving the data to the model as the predictions. Then topic modeling is performed on the preprocessed data by integrating the framework of LDA topic modeling for more optimal classification of topics in the documents.
Keywords
Topic Modeling, LDA, Topics, Natural Language processing, Preprocessing.
Cite as
Abisheka Pon, C. Deisy and P. Sharmila, "A Case-Study on Topic Modeling Approach with Latent Dirichlet Allocation (LDA) Model", In: Rahul Srivastava and Aditya Kr. Singh Pundir (eds), New Frontiers in Communication and Intelligent Systems, SCRS, India, 2022, pp. 291-299. https://doi.org/10.52458/978-81-95502-00-4-30