Concept and Preliminary Testing of the Two-Stage Technology of Terminology Extraction on the Basis of Topic Modeling and Context Analysis

The paper deals with the task of automated terminology extraction. A two-stage technology for its solution is proposed, based on topic modeling and analyzing the context of the use of lexical units. The results of experimental verification of the technology and the prospects for its further development are presented.

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Analysis and Visualization Latent Topic on COVID-19 Vaccine Tweet use two-stage topic modeling (Preprint)

10.2196/preprints.30290 ◽

2021 ◽

Author(s):

Faizah Faizah ◽

Bor-Shen Lin

Keyword(s):

Topic Modeling ◽

Public Perception ◽

Latent Dirichlet Allocation ◽

World Health ◽

Two Stage ◽

The Public ◽

Global Pandemic ◽

Difficult Time ◽

Latent Topic ◽

Latent Topics

BACKGROUND The World Health Organization (WHO) declared COVID-19 as a global pandemic on January 30, 2020. However, the pandemic has not been over yet. Furthermore, in the first quartal of 2021, some countries face the third wave of the pandemic. During the difficult time, the development of the vaccines for COVID-19 accelerates rapidly. Understanding the public perception of the COVID-19 Vaccine according to the data collected from social media can widen the perspective on the state of the global pandemic OBJECTIVE This study explores and analyzes the latent topic on COVID-19 Vaccine Tweet posted by individuals from various countries by using two-stage topic modeling. METHODS A two-stage analysis in topic modeling was proposed to investigating people’s reactions in five countries. The first stage is Latent Dirichlet Allocation that produces the latent topics with the corresponding term distributions that facilitate the investigators to understand the main issues or opinions. The second stage then performs agglomerative clustering on the latent topics based on Hellinger distance, which merges close topics hierarchically into topic clusters to visualize those topics in either tree or graph views. RESULTS In general, the topic discussion regarding the COVID-19 Vaccine in five countries is similar. Topic themes such as "first vaccine" and & "vaccine effect" dominate the public discussion. The remarkable point is that people in some countries have some topic themes, such as "politician opinion" and " stay home" in Canada, "emergency" in India, and & "blood clots" in the United Kingdom. The analysis also shows the most popular COVID-19 Vaccine, which is gaining more public interest. CONCLUSIONS With LDA and Hierarchical clustering, two-stage topic modeling is powerful for visualizing the latent topics and understanding the public perception regarding the COVID-19 Vaccine.

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A study on conceptual associations of colors in preschool children

Pegem Eğitim ve Öğretim Dergisi ◽

10.14527/pegegog.2019.024 ◽

2019 ◽

Vol 9 (3) ◽

pp. 729-764

Author(s):

Özge Mazlum ◽

Fehmi Soner Mazlum

Keyword(s):

Preschool Children ◽

Working Group ◽

Sampling Method ◽

Learning Activities ◽

Preliminary Review ◽

Two Stage ◽

Context Analysis ◽

Purposive Sampling ◽

Analysis Technique ◽

Descriptive Survey

In this study, the conceptual associations of colors in preschool children were examined with an interdisciplinary perspective. Designed as a preliminary review, this study provides insights and suggestions about how conceptual associations of colors can be used for developing products and services for kids and improving the effectiveness of learning activities in education. This study was designed as descriptive survey because it describes an existing situation. This research’s working group was chosen through a purposive sampling method. The study also includes interpreted components. Two-stage interviews were made with 204 children aged between 60 and 72 months in pre-school education in Ankara with active participation of their form teachers, and the data were collected using the context analysis technique. The study found that children show dominant preference for certain colors in connection with certain concepts and they made consistent spectrum preference for certain concepts. These preferences indicate that the children aged between 60 and 72 months are able to make associations between concepts and colors and attribute meanings to colors in the background, with important hints for the use of colors in designing products and planning learning activities for children.

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Track Iran's national COVID-19 response committee’s major concerns using two-stage unsupervised topic modeling

International Journal of Medical Informatics ◽

10.1016/j.ijmedinf.2020.104309 ◽

2021 ◽

Vol 145 ◽

pp. 104309

Author(s):

Fatemeh Kaveh-Yazdy ◽

Sajjad Zarifzadeh

Keyword(s):

Topic Modeling ◽

Two Stage

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Supervised Latent Dirichlet Allocation with Covariates: A Bayesian Structural and Measurement Model of Text and Covariates

10.31234/osf.io/62tc3 ◽

2021 ◽

Author(s):

Kenneth Tyler Wilcox ◽

Ross Jacobucci ◽

Zhiyong Zhang ◽

Brooke A. Ammerman

Keyword(s):

Latent Variables ◽

Topic Modeling ◽

Latent Variable ◽

Latent Dirichlet Allocation ◽

R Package ◽

Measurement Model ◽

Psychological Research ◽

Two Stage ◽

Modeling Approaches ◽

Dirichlet Allocation

Text is a burgeoning data source for psychological researchers, but little methodological research has focused on adapting popular modeling approaches for text to the context of psychological research. One popular measurement model for text, topic modeling, uses a latent mixture model to represent topics underlying a body of documents. Recently, psychologists have studied relationships between these topics and other psychological measures by using estimates of the topics as regression predictors along with other manifest variables. While similar two-stage approaches involving estimated latent variables are known to yield biased estimates and incorrect standard errors, two-stage topic modeling approaches have received limited statistical study and, as we show, are subject to the same problems. To address these problems, we proposed a novel statistical model --- supervised latent Dirichlet allocation with covariates (SLDAX) --- that jointly incorporates a latent variable measurement model of text and a structural regression model to allow the latent topics and other manifest variables to serve as predictors of an outcome. Using a simulation study with data characteristics consistent with psychological text data, we found that SLDAX estimates were generally more accurate and more efficient. To illustrate the application of SLDAX and a two-stage approach, we provide an empirical clinical application to compare the application of both the two-stage and SLDAX approaches. Finally, we implemented the SLDAX model in an open-source R package to facilitate its use and further study.

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Two-stage self-seeding method for preparation of single crystals of poly(phenylene sulfide)

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100153816 ◽

1989 ◽

Vol 47 ◽

pp. 368-369

Author(s):

Sengshiu Chung ◽

Peggy Cebe

Keyword(s):

Single Crystals ◽

High Performance ◽

Dilute Solution ◽

Two Stage ◽

Annealing Behavior ◽

High Performance Polymers ◽

Seeding Method ◽

Phenylene Sulfide

We are studying the crystallization and annealing behavior of high performance polymers, like poly(p-pheny1ene sulfide) PPS, and poly-(etheretherketone), PEEK. Our purpose is to determine whether PPS, which is similar in many ways to PEEK, undergoes reorganization during annealing. In an effort to address the issue of reorganization, we are studying solution grown single crystals of PPS as model materials.Observation of solution grown PPS crystals has been reported. Even from dilute solution, embrionic spherulites and aggregates were formed. We observe that these morphologies result when solutions containing uncrystallized polymer are cooled. To obtain samples of uniform single crystals, we have used two-stage self seeding and solution replacement techniques.

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