scholarly journals Identifying Topical Coverages of Curricula using Topic Modeling and Visualization Techniques: A Case of Digital and Data Curation

2019 ◽  
Vol 14 (1) ◽  
pp. 62-87
Author(s):  
Seungwon Yang ◽  
Boryung Ju ◽  
Haeyong Chung
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Data Curation ◽  
Digital Data ◽  
Certificate Programs ◽  
Emerging Trends ◽  
Probabilistic Topic Modeling ◽  
Core Courses ◽  
Interactive Visualizations ◽  
Visualization Techniques

Digital/data curation curricula have been around for a couple of decades. Currently, several ALA-accredited LIS programs offer digital/data curation courses and certificate programs to address the high demand for professionals with the knowledge and skills to handle digital content and research data in an ever-changing information environment.  In this study, we aimed to examine the topical scopes of digital/data curation curricula in the context of the LIS field.  We collected 16 syllabi from the digital/data curation courses, as well as textual descriptions of the 11 programs and their core courses offered in the U.S., Canada, and the U.K. The collected data were analyzed using a probabilistic topic modeling technique, Latent Dirichlet Allocation, to identify both common and unique topics. The results are the identification of 20 topics both at the program- and course-levels. Comparison between the program- and course-level topics uncovered a set of unique topics, and a number of common topics.  Furthermore, we provide interactive visualizations for digital/data curation programs and courses for further analysis of topical distributions. We believe that our combined approach of a topic modeling and visualizations may provide insight for identifying emerging trends and co-occurrences of topics among digital/data curation curricula in the LIS field.

scholarly journals Exposing Emerging Trends in Smart Sustainable City Research Using Deep Autoencoders-Based Fuzzy C-Means

2021 ◽  
Vol 13 (5) ◽  
pp. 2876
Author(s):  
Anne Parlina ◽  
Kalamullah Ramli ◽  
Hendri Murfi
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Smart Cities ◽  
Detection Methods ◽  
Sustainable City ◽  
Fuzzy C Means ◽  
Progressive Development ◽  
Emerging Trends ◽  
Energy Environment ◽  
Non Negative Matrix Factorization

The literature discussing the concepts, technologies, and ICT-based urban innovation approaches of smart cities has been growing, along with initiatives from cities all over the world that are competing to improve their services and become smart and sustainable. However, current studies that provide a comprehensive understanding and reveal smart and sustainable city research trends and characteristics are still lacking. Meanwhile, policymakers and practitioners alike need to pursue progressive development. In response to this shortcoming, this research offers content analysis studies based on topic modeling approaches to capture the evolution and characteristics of topics in the scientific literature on smart and sustainable city research. More importantly, a novel topic-detecting algorithm based on the deep learning and clustering techniques, namely deep autoencoders-based fuzzy C-means (DFCM), is introduced for analyzing the research topic trend. The topics generated by this proposed algorithm have relatively higher coherence values than those generated by previously used topic detection methods, namely non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), and eigenspace-based fuzzy C-means (EFCM). The 30 main topics that appeared in topic modeling with the DFCM algorithm were classified into six groups (technology, energy, environment, transportation, e-governance, and human capital and welfare) that characterize the six dimensions of smart, sustainable city research.

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 STABILITY OF TOPIC MODELING VIA MODALITY REGULARIZATION

2020 ◽  
Author(s):  
R. Derbanosov ◽  
◽  
M. Bakhanova ◽  
◽  
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Topic Model ◽  
Side Information ◽  
Auxiliary Information ◽  
Discrete Distributions ◽  
Probabilistic Latent Semantic Analysis ◽  
Probabilistic Topic Modeling ◽  
Random Initialization

Probabilistic topic modeling is a tool for statistical text analysis that can give us information about the inner structure of a large corpus of documents. The most popular models—Probabilistic Latent Semantic Analysis and Latent Dirichlet Allocation—produce topics in a form of discrete distributions over the set of all words of the corpus. They build topics using an iterative algorithm that starts from some random initialization and optimizes a loss function. One of the main problems of topic modeling is sensitivity to random initialization that means producing significantly different solutions from different initial points. Several studies showed that side information about documents may improve the overall quality of a topic model. In this paper, we consider the use of additional information in the context of the stability problem. We represent auxiliary information as an additional modality and use BigARTM library in order to perform experiments on several text collections. We show that using side information as an additional modality improves topics stability without significant quality loss of the model.

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.

Topic Modeling Using Latent Dirichlet allocation

2022 ◽  
Vol 54 (7) ◽  
pp. 1-35
Author(s):  
Uttam Chauhan ◽  
Apurva Shah
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Research Work ◽  
Topic Models ◽  
Small Subset ◽  
Distributed Environment ◽  
Future Directions ◽  
Probabilistic Topic Modeling ◽  
Modeling Techniques ◽  
Evaluation Techniques

We are not able to deal with a mammoth text corpus without summarizing them into a relatively small subset. A computational tool is extremely needed to understand such a gigantic pool of text. Probabilistic Topic Modeling discovers and explains the enormous collection of documents by reducing them in a topical subspace. In this work, we study the background and advancement of topic modeling techniques. We first introduce the preliminaries of the topic modeling techniques and review its extensions and variations, such as topic modeling over various domains, hierarchical topic modeling, word embedded topic models, and topic models in multilingual perspectives. Besides, the research work for topic modeling in a distributed environment, topic visualization approaches also have been explored. We also covered the implementation and evaluation techniques for topic models in brief. Comparison matrices have been shown over the experimental results of the various categories of topic modeling. Diverse technical challenges and future directions have been discussed.

scholarly journals Improving Topic Coherence Using Entity Extraction Denoising

2018 ◽  
Vol 110 (1) ◽  
pp. 85-101 ◽  
Author(s):  
Ronald Cardenas ◽  
Kevin Bello ◽  
Alberto Coronado ◽  
Elizabeth Villota
Keyword(s):  
Topic Modeling ◽  
Human Perception ◽  
Relevant Information ◽  
Entity Recognition ◽  
Entity Extraction ◽  
Fine Grained ◽  
Job Advertisement ◽  
Coherence Score ◽  
Probabilistic Topic Modeling ◽  
Promising Solution

Abstract Managing large collections of documents is an important problem for many areas of science, industry, and culture. Probabilistic topic modeling offers a promising solution. Topic modeling is an unsupervised machine learning method and the evaluation of this model is an interesting problem on its own. Topic interpretability measures have been developed in recent years as a more natural option for topic quality evaluation, emulating human perception of coherence with word sets correlation scores. In this paper, we show experimental evidence of the improvement of topic coherence score by restricting the training corpus to that of relevant information in the document obtained by Entity Recognition. We experiment with job advertisement data and find that with this approach topic models improve interpretability in about 40 percentage points on average. Our analysis reveals as well that using the extracted text chunks, some redundant topics are joined while others are split into more skill-specific topics. Fine-grained topics observed in models using the whole text are preserved.

Enhanced context-aware recommendation using topic modeling and particle swarm optimization

2021 ◽  
pp. 1-16
Author(s):  
Ibtissem Gasmi ◽  
Mohamed Walid Azizi ◽  
Hassina Seridi-Bouchelaghem ◽  
Nabiha Azizi ◽  
Samir Brahim Belhaouari
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
State Of The Art ◽  
Weighting Function ◽  
Contextual Factors ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Pso Algorithm ◽  
Context Aware ◽  
Proposed Model

Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques.

scholarly journals A Study on the Characteristics of Academic Topics Related to Renewable Energy Using the Structural Topic Modeling and the Weak Signal Concept

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1497
Author(s):  
Chankook Park ◽  
Minkyu Kim
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Topic Modeling ◽  
Academic Research ◽  
High Rate ◽  
Weak Signals ◽  
Energy Potential ◽  
Rate Of Increase ◽  
Probabilistic Topic Modeling ◽  
To Receive

It is important to examine in detail how the distribution of academic research topics related to renewable energy is structured and which topics are likely to receive new attention in the future in order for scientists to contribute to the development of renewable energy. This study uses an advanced probabilistic topic modeling to statistically examine the temporal changes of renewable energy topics by using academic abstracts from 2010–2019 and explores the properties of the topics from the perspective of future signs such as weak signals. As a result, in strong signals, methods for optimally integrating renewable energy into the power grid are paid great attention. In weak signals, interest in large-capacity energy storage systems such as hydrogen, supercapacitors, and compressed air energy storage showed a high rate of increase. In not-strong-but-well-known signals, comprehensive topics have been included, such as renewable energy potential, barriers, and policies. The approach of this study is applicable not only to renewable energy but also to other subjects.

scholarly journals Mining Open Government Data for Business Intelligence Using Data Visualization: A Two-Industry Case Study

2021 ◽  
Vol 16 (4) ◽  
pp. 1042-1065
Author(s):  
Anne Gottfried ◽  
Caroline Hartmann ◽  
Donald Yates
Keyword(s):  
Data Visualization ◽  
Business Intelligence ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Open Government ◽  
Open Government Data ◽  
Market Opportunities ◽  
Government Data ◽  
Source Of Information

The business intelligence (BI) market has grown at a tremendous rate in the past decade due to technological advancements, big data and the availability of open source content. Despite this growth, the use of open government data (OGD) as a source of information is very limited among the private sector due to a lack of knowledge as to its benefits. Scant evidence on the use of OGD by private organizations suggests that it can lead to the creation of innovative ideas as well as assist in making better informed decisions. Given the benefits but lack of use of OGD to generate business intelligence, we extend research in this area by exploring how OGD can be used to generate business intelligence for the identification of market opportunities and strategy formulation; an area of research that is still in its infancy. Using a two-industry case study approach (footwear and lumber), we use latent Dirichlet allocation (LDA) topic modeling to extract emerging topics in these two industries from OGD, and a data visualization tool (pyLDAVis) to visualize the topics in order to interpret and transform the data into business intelligence. Additionally, we perform an environmental scanning of the environment for the two industries to validate the usability of the information obtained. The results provide evidence that OGD can be a valuable source of information for generating business intelligence and demonstrate how topic modeling and visualization tools can assist organizations in extracting and analyzing information for the identification of market opportunities.

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