Research Topic Analysis of the Journal of Sport Management using Topic Modeling

2019 ◽  
Vol 24 (4) ◽  
pp. 19-33
Author(s):  
Hyungil Kwon ◽  
Juhae Baeck ◽  
Mihwa Choi
Author(s):  
Андрей Иванович Пьянзин ◽  
Надежда Николаевна Пьянзина

На сегодняшний день имеет место противоречие между большим объемом эмпирического и статистического материала по достижениям чувашских спортсменов в крупнейших международных соревнованиях и недостаточно глубоким их анализом, лежащим в основе выявления закономерностей и перспектив развития спорта в республике. Целью исследования является выявление динамики и закономерностей участия спортсменов Чувашской Республики в составе сборной команды страны на Олимпийских играх 1952-2016 гг. Методы исследования: теоретический анализ исторической и справочной литературы по теме исследования, анализ документальных материалов, методы математической статистики. В советский период наибольшее число участников Олимпийских игр среди спортсменов Чувашской Республики (по 4 человека) приходится на игры 1972 и 1976 гг. Самыми успешными можно признать выступления спортсменов Чувашской Республики на Олимпийских играх 1968, 1976 и 1980 гг. Наиболее высокое среднее место приходится на бокс, фехтование, командную велогонку, борьбу классическую. В постсоветский период представительство спортсменов Чувашской Республики в составе Олимпийской сборной России заметно увеличилось и составило по 10 человек на играх в 2000 и 2004 гг., 9 человек на играх в 2008 г. Можно выделить 20-летний период успешного выступления спортсменов Чувашской Республики на Олимпийских играх - с 1992 по 2012 гг. Наиболее высокое среднее место приходится на спортивную гимнастику, бег 3000 м с препятствиями. Today, there is a contradiction between a large volume of empirical and statistical material on the achievements of athletes of the Chuvash Republic in major international competitions and insufficient analysis, which is the basis for identifying the regularities and prospects for the development of sports in the republic. The aim of the study is to identify the dynamics and patterns of participation of athletes of the Chuvash Republic in the national team at the Olympic Games of 1952-2016. The research methods are theoretical analysis of historical and reference literature on the research topic, analysis of documentary materials, methods of mathematical statistics. In the Soviet period, the largest number of participants in the Olympic Games among athletes of the Chuvash Republic (4 people) was in the 1972 and 1976 Games. The performances of the Chuvash athletes at the Olympic Games of 1968, 1976 and 1980 can be considered the most successful. The most striking results were achieved in boxing, fencing, team cycling, and Greco-Roman wrestling. In the post-Soviet period, the representation of athletes of the Chuvash Republic in the Russian Olympic team has noticeably increased and amounted to 10 people at the games in 2000 and 2004, 9 people - at the games in 2008. A 20-year period from 1992 to 2012 can be considered most successful. The greatest results were achieved in gymnastics and steeplechase.


2018 ◽  
Vol 03 (04) ◽  
pp. 1850016 ◽  
Author(s):  
Jin Ho Kim ◽  
Weiru Chen

Traditional journal analyses of topic trends in IS journals have manually coded target articles from chosen time periods. However, some research efforts have been made to apply automatic bibliometric approaches, such as cluster analysis and probabilistic models, to find topics in academic articles in other research areas. The purpose of this study is thus to investigate research topic trends in Engineering Management from 1998 through 2017 using an LDA analysis model. By investigating topics in EM journals, we provide partial but meaningful trends in EM research topics. The trend analysis shows that there are hot topics with increasing numbers of articles, steady topics that remain constant, and cold topics with decreasing numbers of articles.


2020 ◽  
Vol 11 (2) ◽  
pp. 170-181
Author(s):  
Hanna Kholod ◽  
◽  

Problem.The article is a continuation of the study of the processes of decanonization of the interview genre in the early 1990s. It clarifies the specifics of the interviews published in the newspaper "Kinokurer" (1993), through the analysis of materials at the semantic, compositional, figurative, linguistic, paralinguistic levels.Methodology. The descriptive method allowed to record everything that is needed to cover the research topic. Analysis and synthesis contributed to the formation of a holistic view of the specifics of the content, composition, design, image system of interviews published in the newspaper "Kinokurer" (1993). The object of the research is an interview in the newspaper "Kinokurer" (1993). The subject of the research is the specifics of the interview in the newspaper "Kinokurer" (1993). The purpose of the study is to find out the peculiarities of the interview in the newspaper "Kinokurer" (1993).Conclusions.Thus, in the newspaper "Kinokurier" (1993), in addition to classic interviews with different compositional variability and interviews-monologues, journalistic texts are used, in which the interview is dominant and which have the following modifications: genre combination, genre conglomerate, genre inlay, contributing to the maximum presentation of the image of the interviewee on the semantic, compositional, speech, paralinguistic levels. In journalistic texts, where there are interviews of foreign origin and textual additions of domestic journalists, the range of disclosure of the image of the interviewee increases due to the intercultural component.


2019 ◽  
Vol 10 (3) ◽  
pp. e576 ◽  
Author(s):  
Alexandra Lesnikowski ◽  
Ella Belfer ◽  
Emma Rodman ◽  
Julie Smith ◽  
Robbert Biesbroek ◽  
...  

AI ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 578-599
Author(s):  
Fuad Alattar ◽  
Khaled Shaalan

Comparing two sets of documents to identify new topics is useful in many applications, like discovering trending topics from sets of scientific papers, emerging topic detection in microblogs, and interpreting sentiment variations in Twitter. In this paper, the main topic-modeling-based approaches to address this task are examined to identify limitations and necessary enhancements. To overcome these limitations, we introduce two separate frameworks to discover emerging topics through a filtered latent Dirichlet allocation (filtered-LDA) model. The model acts as a filter that identifies old topics from a timestamped set of documents, removes all documents that focus on old topics, and keeps documents that discuss new topics. Filtered-LDA also genuinely reduces the chance of using keywords from old topics to represent emerging topics. The final stage of the filter uses multiple topic visualization formats to improve human interpretability of the filtered topics, and it presents the most-representative document for each topic.


2021 ◽  
Vol 25 ◽  
pp. 100236
Author(s):  
Jiaying Liu ◽  
Hansong Nie ◽  
Shihao Li ◽  
Xiangtai Chen ◽  
Huazhu Cao ◽  
...  

2020 ◽  
Vol 10 (3) ◽  
pp. 834
Author(s):  
Erdenebileg Batbaatar ◽  
Van-Huy Pham ◽  
Keun Ho Ryu

The hallmarks of cancer represent an essential concept for discovering novel knowledge about cancer and for extracting the complexity of cancer. Due to the lack of topic analysis frameworks optimized specifically for cancer data, the studies on topic modeling in cancer research still have a strong challenge. Recently, deep learning (DL) based approaches were successfully employed to learn semantic and contextual information from scientific documents using word embeddings according to the hallmarks of cancer (HoC). However, those are only applicable to labeled data. There is a comparatively small number of documents that are labeled by experts. In the real world, there is a massive number of unlabeled documents that are available online. In this paper, we present a multi-task topic analysis (MTTA) framework to analyze cancer hallmark-specific topics from documents. The MTTA framework consists of three main subtasks: (1) cancer hallmark learning (CHL)—used to learn cancer hallmarks on existing labeled documents; (2) weak label propagation (WLP)—used to classify a large number of unlabeled documents with the pre-trained model in the CHL task; and (3) topic modeling (ToM)—used to discover topics for each hallmark category. In the CHL task, we employed a convolutional neural network (CNN) with pre-trained word embedding that represents semantic meanings obtained from an unlabeled large corpus. In the ToM task, we employed a latent topic model such as latent Dirichlet allocation (LDA) and probabilistic latent semantic analysis (PLSA) model to catch the semantic information learned by the CNN model for topic analysis. To evaluate the MTTA framework, we collected a large number of documents related to lung cancer in a case study. We also conducted a comprehensive performance evaluation for the MTTA framework, comparing it with several approaches.


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