scholarly journals GLOBAL FINANCIAL CRISIS AND TRADE PAPERS: TOPIC ANALYSIS VIA LATENT DIRICHLET ALLOCATION MODEL

2021 ◽  
Author(s):  
Halil ŞİMDİ ◽  
Büşra GARİP
Keyword(s):  
Financial Crisis ◽  
Latent Dirichlet Allocation ◽  
Global Financial Crisis ◽  
Allocation Model ◽  
Topic Analysis ◽  
Latent Dirichlet Allocation Model ◽  
Dirichlet Allocation

scholarly journals Speeding up calibration of latent Dirichlet allocation model to improve topic analysis in software engineering

2021 ◽  
Author(s):  
Jorge Arturo Lopez
Keyword(s):  
Software Engineering ◽  
Simple Formula ◽  
Latent Dirichlet Allocation ◽  
Allocation Model ◽  
Topic Analysis ◽  
Latent Dirichlet Allocation Model ◽  
Related Text ◽  
The Empirical Analysis ◽  
Large Corpus ◽  
Dirichlet Allocation

Extraction of topics from large text corpuses helps improve Software Engineering (SE) processes. Latent Dirichlet Allocation (LDA) represents one of the algorithmic tools to understand, search, exploit, and summarize a large corpus of data (documents), and it is often used to perform such analysis. However, calibration of the models is computationally expensive, especially if iterating over a large number of topics. Our goal is to create a simple formula allowing analysts to estimate the number of topics, so that the top X topics include the desired proportion of documents under study. We derived the formula from the empirical analysis of three SE-related text corpuses. We believe that practitioners can use our formula to expedite LDA analysis. The formula is also of interest to theoreticians, as it suggests that different SE text corpuses have similar underlying properties.

scholarly journals Speeding up calibration of latent Dirichlet allocation model to improve topic analysis in software engineering

2021 ◽  
Author(s):  
Jorge Arturo Lopez
Keyword(s):  
Software Engineering ◽  
Simple Formula ◽  
Latent Dirichlet Allocation ◽  
Allocation Model ◽  
Topic Analysis ◽  
Latent Dirichlet Allocation Model ◽  
Related Text ◽  
The Empirical Analysis ◽  
Large Corpus ◽  
Dirichlet Allocation

Extraction of topics from large text corpuses helps improve Software Engineering (SE) processes. Latent Dirichlet Allocation (LDA) represents one of the algorithmic tools to understand, search, exploit, and summarize a large corpus of data (documents), and it is often used to perform such analysis. However, calibration of the models is computationally expensive, especially if iterating over a large number of topics. Our goal is to create a simple formula allowing analysts to estimate the number of topics, so that the top X topics include the desired proportion of documents under study. We derived the formula from the empirical analysis of three SE-related text corpuses. We believe that practitioners can use our formula to expedite LDA analysis. The formula is also of interest to theoreticians, as it suggests that different SE text corpuses have similar underlying properties.

Research Topic Analysis in Engineering Management Using a Latent Dirichlet Allocation Model

2018 ◽  
Vol 03 (04) ◽  
pp. 1850016 ◽  
Author(s):  
Jin Ho Kim ◽  
Weiru Chen
Keyword(s):  
Latent Dirichlet Allocation ◽  
Probabilistic Models ◽  
Engineering Management ◽  
Research Topic ◽  
Analysis Model ◽  
Research Topics ◽  
Allocation Model ◽  
Topic Analysis ◽  
Latent Dirichlet Allocation Model ◽  
Research Areas

Traditional journal analyses of topic trends in IS journals have manually coded target articles from chosen time periods. However, some research efforts have been made to apply automatic bibliometric approaches, such as cluster analysis and probabilistic models, to find topics in academic articles in other research areas. The purpose of this study is thus to investigate research topic trends in Engineering Management from 1998 through 2017 using an LDA analysis model. By investigating topics in EM journals, we provide partial but meaningful trends in EM research topics. The trend analysis shows that there are hot topics with increasing numbers of articles, steady topics that remain constant, and cold topics with decreasing numbers of articles.

Fuzzy-Gibbs latent Dirichlet allocation model for feature extraction on Indonesian documents

2017 ◽  
Vol 10 ◽  
pp. 403-421 ◽  
Author(s):  
Putu Manik Prihatini ◽  
I Ketut Gede Darma Putra ◽  
Ida Ayu Dwi Giriantari ◽  
Made Sudarma
Keyword(s):  
Feature Extraction ◽  
Latent Dirichlet Allocation ◽  
Allocation Model ◽  
Latent Dirichlet Allocation Model ◽  
Dirichlet Allocation
Sign in / Sign up

Export Citation Format

Share Document