Improvement of the KTDA Algorithm for the Visualization of Semantic Network

Author(s)
표성인
Advisor
권순선
Department
일반대학원 수학과
Publisher
The Graduate School, Ajou University
Publication Year
2023-02
Language
eng
Keyword
Correlation TestingKorean Text Data AnalysisLDA Topic ModelingSemantic NetworkSparsity CutoffText MiningVisualization
Alternative Abstract
Textual data differs in the analysis method depending on its domain or various characteristics. The Korean Text Data Analysis Algorithm was presented to provide a pipeline for statistical analysis of Korean text for the above reasons. However, in the process of dimension reduction and correlation cutting, a cutoff setting with insufficient statistical inference was accompanied. The dense visualization result also weaken the interpretabiltiy of the plot. To improve the algorithm, this study presented statistical inference for word-to-word relationships using FDR(False Discovery Rate) control and improved dimension reduction and visualization by applying sparsity cutoff setting and LDA(Latent Dirichlet Allocation). New algorithm is expected to improve the reliability and interpretation of the results of analysis.
URI
https://dspace.ajou.ac.kr/handle/2018.oak/24695
Fulltext

Appears in Collections:
Graduate School of Ajou University > Department of Mathematics > 3. Theses(Master)
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse