Tomoe: topic modelling web application / Rohis Rachman, Jihad Nurul Islami and Nur’eni

Rachman, Rohis and Nurul Islami, Jihad and Nur’eni, Nur’eni (2023) Tomoe: topic modelling web application / Rohis Rachman, Jihad Nurul Islami and Nur’eni. In: International Jasin Multimedia & Computer Science Invention and Innovation Exhibition (i-JaMCSIIX 2023). Faculty of Computer and Mathematical Sciences, Kampus Jasin, pp. 153-156. ISBN 978-967-15337-0-3 (Submitted)

Abstract

Text data is becoming a very valuable asset in digital era in various fields. However, managing and analyzing text data becomes increasingly impossible as information continues to grow. Therefore, NLP methods can be applied. One of the application of NLP is Topic Modeling, which is a method that can find and identify hidden topics in text documents. The method of Topic Modeling that is often used is LDA. LDA is an unattended AI model using a soft fuzzy clustering approach. The assumption built from this model is that the document consists of topics composed of lists of words. Unfortunately, in its implementation, doing data analysis with Topic Modeling requires quite a lot of time and deeper learning. So that an AI Web Application was created based on the Topic Modeling method called Tomoe (Topic Modelling Web Application) to facilitate the summarization of text documents. In using this application users do not need to worry about data theft, because this application does not use a database system. The results of the analysis of this application are in the form of an Initial Word Cloud that shows the most frequently appearing words based on their font size, Topics in Text is the result of topic modeling based on the LDA model and Word Cloud from Topics is a visualization of Topics in Text. So that the use of Tomoe can certainly make it easier for users to model topics or see the subject matter of one text document more quickly and easily.

Metadata

Item Type: Book Section
Creators:
Creators
Email / ID Num.
Rachman, Rohis
rohisrachman@gmail.com
Nurul Islami, Jihad
jihadnurul16@gmail.com
Nur’eni, Nur’eni
nureniuntad@gmail.com
Contributors:
Contribution
Name
Email / ID Num.
Patron
Md Badarudin, Ismadi
UNSPECIFIED
Advisor
Jasmis, Jamaluddin
UNSPECIFIED
Advisor
Jono, Mohd Hajar Hasrol
UNSPECIFIED
Director
Suhaimi, Nur Suhailayani
UNSPECIFIED
Team Member
Mat Zain, Nurul Hidayah
UNSPECIFIED
Team Member
Abdullah Sani, Anis Shobirin
UNSPECIFIED
Team Member
Halim, Faiqah Hafidzah
UNSPECIFIED
Team Member
Abd Kadir, Siti Aisyah
UNSPECIFIED
Team Member
Jalil, Ummu Mardhiah
UNSPECIFIED
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunication > Blogs. Weblog software
Divisions: Universiti Teknologi MARA, Melaka > Jasin Campus > Faculty of Computer and Mathematical Sciences
Event Title: International Jasin Multimedia & Computer Science Invention and Innovation Exhibition (i-JaMCSIIX 2023)
Event Dates: 8th November 2023
Page Range: pp. 153-156
Keywords: Natural Language Preprocessing (NLP); Latent Dirichlet Allocation (LDA); Topic modeling
Date: 2023
URI: https://ir.uitm.edu.my/id/eprint/94387
Edit Item
Edit Item

Download

[thumbnail of Extended Abstract] Text (Extended Abstract)
94387.pdf

Download (2MB)

ID Number

94387

Indexing

Statistic

Statistic details