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.

A Topic Modeling Analysis of Research Trends on Trauma in Korea

2017 ◽  
Vol 13 (10) ◽  
pp. 103-123
Author(s):  
Hye Sun Joo ◽  
◽  
Soo Sang Lee ◽  
Hyun Nie Ahn ◽  
◽  
...  
Keyword(s):  
Topic Modeling ◽  
Research Trends ◽  
Modeling Analysis

scholarly journals Enterprises' perception and practice of humane entrepreneurship

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anna Dębicka ◽  
Karolina Olejniczak ◽  
Joanna Skąpska
Keyword(s):  
Single Case ◽  
Business Practice ◽  
Actual State ◽  
Content Type ◽  
Exploratory Stage ◽  
Management Hierarchy ◽  
Trends Over Time ◽  
New Research ◽  
Quantitative And Qualitative Methods

PurposeAs a new concept for humane entrepreneurship (HumEnt) evolves, many new research questions arise. At the exploratory stage, the authors found it relevant to examine and discuss the perception of the fundamental assumptions of the HumEnt concept and activities undertaken in this area by business practice.Design/methodology/approachTo thoroughly understand the studied phenomenon, a combination of quantitative and qualitative methods was used. An exploratory survey was obtained from 126 purposefully selected enterprises in Poland; then, a single case study was analysed.FindingsThe conducted analysis showed differences between the activities of enterprises and the perception of the HumEnt concept among employees that are especially noticeable at different levels of the management hierarchy.Research limitations/implicationsThe multifaceted nature of the results obtained is limited by the inability to infer international differences, to capture trends over time and to generalise to the total population of enterprises.Practical implicationsAlthough the surveyed companies recognise the importance of the HumEnt concept, it is not tantamount to its execution. The research results may be valuable, especially for smaller enterprises, where the business practice may require support in applying the HumEnt approach.Originality/valueThe research explored both the actual state confirmed by the actions taken and the perception of the importance of individual elements of HumEnt. A knowing–doing gap has been demonstrated between these planes. Moreover, thanks to a two-stage study, practices were selected that can be successfully implemented also in small and medium-sized enterprises.

scholarly journals A Topic Modeling Analysis of TCGA Breast and Lung Cancer Transcriptomic Data

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3799
Author(s):  
Filippo Valle ◽  
Matteo Osella ◽  
Michele Caselle
Keyword(s):  
Lung Cancer ◽  
Topic Modeling ◽  
Relevant Information ◽  
Transcriptomic Data ◽  
New Class ◽  
Modeling Analysis ◽  
Cancer Subtype ◽  
Low Dimensional ◽  
Simple Neural Network ◽  
Topic Structure

Topic modeling is a widely used technique to extract relevant information from large arrays of data. The problem of finding a topic structure in a dataset was recently recognized to be analogous to the community detection problem in network theory. Leveraging on this analogy, a new class of topic modeling strategies has been introduced to overcome some of the limitations of classical methods. This paper applies these recent ideas to TCGA transcriptomic data on breast and lung cancer. The established cancer subtype organization is well reconstructed in the inferred latent topic structure. Moreover, we identify specific topics that are enriched in genes known to play a role in the corresponding disease and are strongly related to the survival probability of patients. Finally, we show that a simple neural network classifier operating in the low dimensional topic space is able to predict with high accuracy the cancer subtype of a test expression sample.

Exploring research trends in big data across disciplines: A text mining analysis

2020 ◽  
pp. 016555152093285
Author(s):  
Ehsan Mohammadi ◽  
Amir Karami
Keyword(s):  
Big Data ◽  
Topic Modeling ◽  
Research Trends ◽  
Academic Disciplines ◽  
Research Trend ◽  
Medical Sciences ◽  
Academic Fields ◽  
Key Topics ◽  
New Research ◽  
Data Analytic

Using big data has been a prevailing research trend in various academic fields. However, no studies have explored the scope and structure of big data across disciplines. In this article, we applied topic modeling and word co-occurrence analysis methods to identify key topics from more than 36,000 big data publications across all academic disciplines between 2012 and 2017. The results revealed several topics associated with the storage, collection and analysis of large datasets; the publications were predominantly published in computational fields. Other identified research topics show the influence of big data methods and techniques in areas beyond computer science, such as education, urban informatics, business, health and medical sciences. In fact, the prevalence of these topics has increased over time. In contrast, some themes like parallel computing, network modeling and big data analytic techniques have lost their popularity in recent years. These results probably reflect the maturity of big data core topics and highlight flourishing new research trends pertinent to big data in new domains, especially in social sciences, health and medicine. Findings of this article can be beneficial for researchers and science policymakers to understand the scope and structure of big data in different academic disciplines.

Research Trends of Consumer Education Using Topic Modeling

2020 ◽  
Vol 16 (2) ◽  
pp. 83-115
Author(s):  
Mira Kim ◽  
◽  
Hye Sun Hwang ◽  
Xu Li
Keyword(s):  
Topic Modeling ◽  
Research Trends ◽  
Consumer Education
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