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.

scholarly journals Identifying Research Topics and Trends in Asset Management for Sustainable Use: A Topic Modeling Approach

2021 ◽  
Vol 13 (9) ◽  
pp. 4792
Author(s):  
Hosang Jung ◽  
Boram Kim
Keyword(s):  
Life Cycle ◽  
Asset Management ◽  
Topic Modeling ◽  
Life Cycle Cost ◽  
Latent Dirichlet Allocation ◽  
Sustainable Use ◽  
Future Research ◽  
Research Topics ◽  
The Past ◽  
Modeling Approach

Asset management is not new, and research has been conducted in private and public sectors on how to systematically maintain infrastructure or facilities for sustainable use and achieve the level of service desired by users or customers at the lowest life cycle costs. This research identifies the research topics and trends in asset management over the past 30 years. To this end, latent Dirichlet allocation, a topic modeling approach, was applied to articles published in engineering journals and investigated the following three research questions: (1) what have the key topics been for the past three decades? (2) what are the main activities and target sectors of asset management? (3) how have the research topics and keywords changed over the past three decades? The analysis shows that the target field of asset management has broadened while the main activities of asset management have been limited to several popular activities such as life cycle cost analysis and reliability analysis. Some implications and future research directions are also discussed.

Review of Perfectionism Research From 1990 to 2019 Utilizing a Text-Mining Approach

2021 ◽  
pp. 108926802110188
Author(s):  
Hanna Suh ◽  
Seoyoung Kim ◽  
Dong-gwi Lee
Keyword(s):  
Text Mining ◽  
Topic Modeling ◽  
Personality Characteristic ◽  
Future Research ◽  
Multinomial Regression ◽  
Research Topics ◽  
Word Analysis ◽  
The Past ◽  
Research Findings ◽  
Meta Analyses

Perfectionism is a personality characteristic that has been explored for its implications in mental health; reviews and meta-analyses were conducted to synthesize research findings. This study systemically synthesizes the perfectionism literature using a text-mining approach. Co-word analysis and Dirichlet Multinomial Regression topic modeling were performed on a total of 1,529 perfectionism abstracts published from 1990 to 2019. Analysis revealed that perfectionism research is closely connected with “disorder,” with “symptom” being the most frequently addressed issue. Topic-modeling results found a total of 15 topics represented perfectionism research of the past three decades. Most articles were published in psychology journals, with social and clinical psychology subdisciplines publishing perfectionism articles most frequently. There were overlaps in research topics by journal subdisciplines, while differences were also observed. This study provides a panoramic view of perfectionism literature and highlights frequently and infrequently explored areas that could be considered in future research endeavors.

scholarly journals Doctoral dissertations in Chinese Interpreting Studies: A scientometric survey using topic modeling

2015 ◽  
Author(s):  
Ziyun Xu
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Cognitive Resources ◽  
The Past ◽  
Career Trajectories ◽  
Probabilistic Topic Modeling ◽  
The Subject ◽  
Number Of Publications ◽  
Doctoral Dissertations ◽  
In Cis

Despite being a relatively new discipline, Chinese Interpreting Studies (CIS) has witnessed tremendous growth in the number of publications and diversity of topics investigated over the past two decades. The number of doctoral dissertations produced has also increased rapidly since the late 1990s. As CIS continues to mature, it is important to evaluate its dominant topics, trends and institutions, as well as the career development of PhD graduates in the subject. In addition to traditional scientometric techniques, this study’s empirical objectivity is heightened by its use of Probabilistic Topic Modeling (PTM), which uses Latent Dirichlet Allocation (LDA) to analyze the topics covered in a near-exhaustive corpus of CIS dissertations. The analysis reveals that the topics of allocation of cognitive resources, deverbalization, and modeling the interpreting process attracted most attention from doctoral researchers. Additional analyses were used to track the research productivity of institutions and the career trajectories of PhD holders: one school was found to stand out, accounting for more than half of the total dissertations produced, and a PhD in CIS was found to be a highly useful asset for new professional interpreters.

scholarly journals A Bibliometric Analysis of 8,276 Publications During the Past 25 Years on Cholangiocarcinoma by Machine Learning

2021 ◽  
Vol 11 ◽  
Author(s):  
Zeyu Zhang ◽  
Zhiming Wang ◽  
Yun Huang
Keyword(s):  
Machine Learning ◽  
Clinical Trials ◽  
Latent Dirichlet Allocation ◽  
Basic Research ◽  
Mesh Term ◽  
General Interest ◽  
Research Topics ◽  
Scientific Publications ◽  
The Past ◽  
Basic Studies

IntroductionCholangiocarcinoma (CCA) is the second most common hepatic malignancy. Progress and developments have also been made in the field of CCA management along with increasing scientific publications during the past decades, which reflect topics of general interest and suggest the future direction of studies. The purpose of this bibliometric study is to summarize scientific publications during the past 25 years in the field of CCA using a machine learning method.Material and MethodsScientific publications focusing on CCA from 1995 to 2019 were searched in PubMed using the MeSH term “cholangiocarcinoma.” Full associated data were downloaded in the format of PubMed and extracted in the R platform. Latent Dirichlet allocation (LDA) was adopted to identify the research topics from the abstract of each publication using Python.ResultsA total of 8,276 publications related to CCA from the last 25 years were found and included in this study. The most type of publications remained little changed, while the proportion of clinical trials remained relatively low (7.24% as the highest) and, more significantly, with a further downward trend during the recent years (1.42% in 2019). Neoplasm staging, hepatectomy, and survival rate were the most concerning terms among those who are diagnosis-related, treatment-related, and prognosis-related. The LDA analyses showed chemotherapy, hepatectomy, and stent as the highly concerned research topics of CCA treatment. Meanwhile, conversions from basic studies to clinical therapies were suggested by a poor connection between clusters of treatment management and basic research.ConclusionThe number of publications of CCA has increased rapidly during the past 25 years. Survival analysis, differential diagnosis, and microRNA expression are the most concerned topics in CCA studies. Besides, there is an urgent need for high-quality clinical trials and conversions from basic studies to clinical therapies.

scholarly journals Social-Child-Case Document Clustering based on Topic Modeling using Latent Dirichlet Allocation

2020 ◽  
Vol 14 (2) ◽  
pp. 179
Author(s):  
Nur Annisa Tresnasari ◽  
Teguh Bharata Adji ◽  
Adhistya Erna Permanasari
Keyword(s):  
Social Workers ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
The Past ◽  
Coherence Score ◽  
The Social ◽  
Case Type ◽  
The Right ◽  
All Treatment ◽  
Dirichlet Allocation

Children are the future of the nation. All treatment and learning they get would affect their future. Nowadays, there are various kinds of social problems related to children.  To ensure the right solution to their problem, social workers usually refer to the social-child-case (SCC) documents to find similar cases in the past and adapting the solution of the cases. Nevertheless, to read a bunch of documents to find similar cases is a tedious task and needs much time. Hence, this work aims to categorize those documents into several groups according to the case type. We use topic modeling with Latent Dirichlet Allocation (LDA) approach to extract topics from the documents and classify them based on their similarities. The Coherence Score and Perplexity graph are used in determining the best model. The result obtains a model with 5 topics that match the targeted case types. The result supports the process of reusing knowledge about SCC handling that ease the finding of documents with similar cases

scholarly journals Doctoral dissertations in Chinese Interpreting Studies: A scientometric survey using topic modeling

2015 ◽  
Author(s):  
Ziyun Xu
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Cognitive Resources ◽  
The Past ◽  
Career Trajectories ◽  
Probabilistic Topic Modeling ◽  
The Subject ◽  
Number Of Publications ◽  
Doctoral Dissertations ◽  
In Cis

Despite being a relatively new discipline, Chinese Interpreting Studies (CIS) has witnessed tremendous growth in the number of publications and diversity of topics investigated over the past two decades. The number of doctoral dissertations produced has also increased rapidly since the late 1990s. As CIS continues to mature, it is important to evaluate its dominant topics, trends and institutions, as well as the career development of PhD graduates in the subject. In addition to traditional scientometric techniques, this study’s empirical objectivity is heightened by its use of Probabilistic Topic Modeling (PTM), which uses Latent Dirichlet Allocation (LDA) to analyze the topics covered in a near-exhaustive corpus of CIS dissertations. The analysis reveals that the topics of allocation of cognitive resources, deverbalization, and modeling the interpreting process attracted most attention from doctoral researchers. Additional analyses were used to track the research productivity of institutions and the career trajectories of PhD holders: one school was found to stand out, accounting for more than half of the total dissertations produced, and a PhD in CIS was found to be a highly useful asset for new professional interpreters.

scholarly journals TOPIK MODELING PENELITIAN DOSEN JPTEI UNY PADA GOOGLE SCHOLAR MENGGUNAKAN LATENT DIRICHLET ALLOCATION

2019 ◽  
Vol 4 (2) ◽  
pp. 154-161
Author(s):  
Akhsin Nurlayli ◽  
Moch. Ari Nasichuddin
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
System Development ◽  
Google Scholar ◽  
Research Topics ◽  
Study Program ◽  
Hierarchical Bayesian Analysis ◽  
Modeling Process ◽  
Vocational Learning ◽  
Dirichlet Allocation

The mapping of research topics for lecturers is necessary to determine the research tendencies in a department or study program. This study aims to implement topic modeling in the publication titles of the Department of Electronics and Informatics Education Engineering of Universitas Negeri Yogyakarta (JPTEI UNY) lecturers taken from Google Scholar. The method used for topic modeling is the Latent Dirichlet Allocation (LDA). LDA is a generative probabilistic model for finding the semantic structure of a corpus collection based on the hierarchical bayesian analysis. After the topic modeling process, the results showed that JPTEI UNY lecturers tend to have four research clusters consisting of vocational education, system development, learning media, and vocational learning systems.

scholarly journals A Study on Ways to Improve Mobile RPG Using Big Data Text Mining

2021 ◽  
Author(s):  
DongHyun Youm ◽  
JungYoon Kim
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Mass Production ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Meaningful Information ◽  
Mining Technique ◽  
Production Type ◽  
Google Play ◽  
Play Experience

As RPG has high sales and profits, lots of developers have supplied various RPG to market but it changed to mass production type with sensational advertising, low quality and excessive charging and similar contents which affects game market and users’ game play experience. The author of this paper studied ways to improve mobile RPG by collecting and analyzing users’ reviews using crawling on Google Play Store. The author of this paper used topic modeling that uses text mining technique and LDA (Latent Dirichlet Allocation) to extract meaningful information from collected big data and visualized it. Inferring users’ reviews, figuring out opinions objectively and seeking ways to improve games are helpful in improving mobile RPG that can be played continuously.

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