ECB and FED Governors’ Speeches: A Topic Modeling Analysis (2007–2019)

2020 ◽  
pp. 9-25
Author(s):  
Marika Carboni ◽  
Vincenzo Farina ◽  
Daniele A. Previati
Keyword(s):  
Topic Modeling ◽  
Modeling Analysis

A Topic Modeling Analysis on the Major Social Issues of the Students’ Human Rights Ordinance in Korea

2017 ◽  
Vol 18 (4) ◽  
pp. 683-711
Author(s):  
Hyun-Jeong Park ◽  
Hanna Kim ◽  
YuJung Hong
Keyword(s):  
Human Rights ◽  
Topic Modeling ◽  
Social Issues ◽  
Modeling Analysis

A topic modeling analysis of Korea’s T&I research trends in the 2010s

Babel ◽  
2021 ◽  
Author(s):  
Changsoo Lee
Keyword(s):  
Topic Modeling ◽  
Research Output ◽  
Research Trends ◽  
Korean Language ◽  
Modeling Analysis ◽  
Trends Over Time ◽  
New Research ◽  
Topic Structure ◽  
Manual Classification

Abstract The present study aims to demonstrate the relevance of topic modeling as a new research tool for analyzing research trends in the T&I field. Until now, most efforts to this end have relied on manual classification based on pre-established typologies. This method is time- and labor-consuming, prone to subjective biases, and limited in describing a vast amount of research output. As a key component of text mining, topic modeling offers an efficient way of summarizing topic structure and trends over time in a collection of documents while being able to describe the entire system without having to rely on sampling. As a case study, the present paper applies the technique to analyzing a collection of abstracts from four Korean Language T&I journals for the 2010s decade (from 2010 to 2019). The analysis proves the technique to be highly successful in uncovering hidden topical structure and trends in the abstract corpus. The results are discussed along with implications of the technique for the T&I field.

scholarly journals Analysis of the Trends in Biochemical Research Using Latent Dirichlet Allocation (LDA)

Processes ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 379 ◽  
Author(s):  
Kang ◽  
Kim ◽  
Kang
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Research Topics ◽  
Helpful Tool ◽  
Journal Editors ◽  
The Past ◽  
Mining Technique ◽  
Modeling Analysis ◽  
Funding Agencies ◽  
Biochemical Research

Biochemistry has been broadly defined as “chemistry of molecules included or related to living systems”, but is becoming increasingly hard to be distinguished from other related fields. Targets of its studies evolve rapidly; some newly emerge, disappear, combine, or resurface themselves with a fresh viewpoint. Methodologies for biochemistry have been extremely diversified, thanks particularly to those adopted from molecular biology, synthetic chemistry, and biophysics. Therefore, this paper adopts topic modeling, a text mining technique, to identify the research topics in the field of biochemistry over the past twenty years and quantitatively analyze the changes in its trends. The results of the topic modeling analysis obtained through this study will provide a helpful tool for researchers, journal editors, publishers, and funding agencies to understand the connections among the diverse sub-fields in biochemical research and even see how the research topics branch out and integrate with other fields.

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