Review of User Comments for the OVO Fintech application using LDA

The main purpose of this article is to model the topic of OVO Fintech user comments using LDA. This research is a word processing research conducted using text mining. The statistical population is the comments of OVO e-wallet Fintech users in 2021 who take data randomly. LDA (Latent Dirichlet Alloc...

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Bibliographic Details
Published in2022 6th International Conference on Information Technology (InCIT) pp. 326 - 330
Main Authors Widiantoro, Albertus Dwiyoga, Harnadi, Bernardinus
Format Conference Proceeding
LanguageEnglish
Published IEEE 10.11.2022
Subjects
Analytical models
Fintech
Latent Dirichlet Allocation
LDA
Maintenance engineering
Online banking
OVO
Resource management
Sociology
Strategic planning
Text mining
Topic model
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Summary:The main purpose of this article is to model the topic of OVO Fintech user comments using LDA. This research is a word processing research conducted using text mining. The statistical population is the comments of OVO e-wallet Fintech users in 2021 who take data randomly. LDA (Latent Dirichlet Allocation) and Python programming language were applied to analyze data and implement text mining algorithms from topic modeling. Findings are the most important keywords in Fintech user comments. Also, there are 6 important topics identified in the comments of Fintech users by applying a topic modeling algorithm.Text mining and Latent Dirichlet Allocation were applied to analyze the e-wallet. OVO is a hot topic for Fintech users. Finally, in addition to a retrospective approach to data collection and analysis, the results can be utilized with a prospective approach to strategic planning and policy making.
DOI:10.1109/InCIT56086.2022.10067746