A computational literature review of football performance analysis through probabilistic topic modeling

2021 ◽  
Author(s):  
Vitor Ayres Principe ◽  
Rodrigo Gomes de Souza Vale ◽  
Juliana Brandão Pinto de Castro ◽  
Luiz Marcelo Carvano ◽  
Roberto André Pereira Henriques ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Literature Review ◽  
Topic Modeling ◽  
Probabilistic Topic Modeling

Conceptualización del desempeño exportador: análisis de los avances en la literatura de los negocios internacionales

Oikos ◽  
2016 ◽  
Vol 19 (40) ◽  
pp. 137
Author(s):  
Roberto Horta
Keyword(s):  
Performance Analysis ◽  
Literature Review ◽  
International Business ◽  
Export Performance ◽  
Comprehensive Literature Review

RESUMENEl objetivo de este artículo es el de analizar el concepto “desempeño exportador”, concepto muy usado en la literatura relacionada con los negocios internacionales, a los efectos de aportar a su conceptualización y medición. Para ello, se efectúa una amplia revisión de la literatura y se analizan los avances realizados por los investigadores a los efectos de aportar a las dimensiones y formas de medir este concepto. Se concluye que se trata de un concepto en el cual siguen existiendo diversos enfoques, aunque existen avances importantes en la forma de operacionalizar el desempeño exportador.Palabras clave: conceptualización, desempeño exportador, negocios internacionales.Conceptualization of export performance: analysis of developments in the literature of international business ABSTRACTThe aim of this paper is to analyze the concept of "export performance" concept very use in the literature of international business, in order to contribute to its conceptualization and measurement. For this, a comprehensive literature review is performed and the progress made by researchers to contribute to the dimensions and ways of measuring this concept is discussed. Concluded that it is a concept which still exist several approaches, although there are significant advances in how to operationalize export performance.Keywords: conceptualization, export performance, international business.Conceptualização do desempenho exportador: a análise dos avanços na literatura dos negócios internacionais RESUMO O objetivo deste artigo é analisar o conceito de "desempenho exportador", conceito utilizado amplamente na literatura sobre negócios internacionais, aos efeitos de contribuir para a conceituação e medição. Para fazer isso, se faz uma extensa revisão da literatura e analisam-se os avanços realizados pelos pesquisadores aos efeitos de contribuir nas dimensões e formas de medir este conceito. Conclui-se que se trata de um conceito no qual continuam existindo diversas abordagens, apesar dos avanços significativos na forma de operacionalizar o desempenho exportador.Palavras-chave: conceituação, desempenho exportador, negócios internacionais.

Investigating Diseases and Chemicals in COVID-19 Literature with Text Mining (Preprint)

2020 ◽  
Author(s):  
Amir Karami ◽  
Brandon Bookstaver ◽  
Melissa Nolan
Keyword(s):  
Text Mining ◽  
Literature Review ◽  
Topic Modeling ◽  
Large Scale ◽  
Clinical Manifestations ◽  
International Health ◽  
Research Papers ◽  
Strategic Plans ◽  
Funding Agencies ◽  
The Relationship

BACKGROUND The COVID-19 pandemic has impacted nearly all aspects of life and has posed significant threats to international health and the economy. Given the rapidly unfolding nature of the current pandemic, there is an urgent need to streamline literature synthesis of the growing scientific research to elucidate targeted solutions. While traditional systematic literature review studies provide valuable insights, these studies have restrictions, including analyzing a limited number of papers, having various biases, being time-consuming and labor-intensive, focusing on a few topics, incapable of trend analysis, and lack of data-driven tools. OBJECTIVE This study fills the mentioned restrictions in the literature and practice by analyzing two biomedical concepts, clinical manifestations of disease and therapeutic chemical compounds, with text mining methods in a corpus containing COVID-19 research papers and find associations between the two biomedical concepts. METHODS This research has collected papers representing COVID-19 pre-prints and peer-reviewed research published in 2020. We used frequency analysis to find highly frequent manifestations and therapeutic chemicals, representing the importance of the two biomedical concepts. This study also applied topic modeling to find the relationship between the two biomedical concepts. RESULTS We analyzed 9,298 research papers published through May 5, 2020 and found 3,645 disease-related and 2,434 chemical-related articles. The most frequent clinical manifestations of disease terminology included COVID-19, SARS, cancer, pneumonia, fever, and cough. The most frequent chemical-related terminology included Lopinavir, Ritonavir, Oxygen, Chloroquine, Remdesivir, and water. Topic modeling provided 25 categories showing relationships between our two overarching categories. These categories represent statistically significant associations between multiple aspects of each category, some connections of which were novel and not previously identified by the scientific community. CONCLUSIONS Appreciation of this context is vital due to the lack of a systematic large-scale literature review survey and the importance of fast literature review during the current COVID-19 pandemic for developing treatments. This study is beneficial to researchers for obtaining a macro-level picture of literature, to educators for knowing the scope of literature, to journals for exploring most discussed disease symptoms and pharmaceutical targets, and to policymakers and funding agencies for creating scientific strategic plans regarding COVID-19.

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.

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 Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4710
Author(s):  
Mariusz Kostrzewski ◽  
Rafał Melnik
Keyword(s):  
Performance Analysis ◽  
Literature Review ◽  
Condition Monitoring ◽  
Systematic Literature Review ◽  
Academic Research ◽  
Rail Transport ◽  
Transport Systems ◽  
Future Research ◽  
Area Of Interest ◽  
Rail Systems

Condition monitoring of rail transport systems has become a phenomenon of global interest over the past half a century. The approaches to condition monitoring of various rail transport systems—especially in the context of rail vehicle subsystem and track subsystem monitoring—have been evolving, and have become equally significant and challenging. The evolution of the approaches applied to rail systems’ condition monitoring has followed manual maintenance, through methods connected to the application of sensors, up to the currently discussed methods and techniques focused on the mutual use of automation, data processing, and exchange. The aim of this paper is to provide an essential overview of the academic research on the condition monitoring of rail transport systems. This paper reviews existing literature in order to present an up-to-date, content-based analysis based on a coupled methodology consisting of bibliometric performance analysis and systematic literature review. This combination of literature review approaches allows the authors to focus on the identification of the most influential contributors to the advances in research in the analyzed area of interest, and the most influential and prominent researchers, journals, and papers. These findings have led the authors to specify research trends related to the analyzed area, and additionally identify future research agendas in the investigation from engineering perspectives.

Learning Hedonic Games via Probabilistic Topic Modeling

2019 ◽  
pp. 62-76 ◽  
Author(s):  
Athina Georgara ◽  
Thalia Ntiniakou ◽  
Georgios Chalkiadakis
Keyword(s):  
Topic Modeling ◽  
Probabilistic Topic Modeling ◽  
Hedonic Games
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