A Study on the CSR Research Trends in the Academic Field of Advertising and Public Relations Using Semantic Network and Topic Modeling (2003-2020)

AbstractThis study explores tweets from Oct 2008 to Oct 2018 related to halal tourism. The tweets were extracted from twitter and underwent various cleaning processes. A total of 33,880 tweets were used for analysis. Analysis intended to (1) identify the topics users tweet about regarding halal tourism, and (2) analyze the emotion-based sentiment of the tweets. To identify and analyze the topics, the study used a word list, concordance graphs, semantic network analysis, and topic-modeling approaches. The NRC emotion lexicon was used to examine the sentiment of the tweets. The analysis illustrated that the word “halal” occurred in the highest number of tweets and was primarily associated with the words “food” and “hotel”. It was also observed that non-Muslim countries such as Japan and Thailand appear to be popular as halal tourist destinations. Sentiment analysis found that there were more positive than negative sentiments among the tweets. The findings have shown that halal tourism is a global market and not only restricted to Muslim countries. Thus, industry players should take the opportunity to use social media to their advantage to promote their halal tourism packages as it is an effective method of communication in this decade.

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Exploring Latent Topics and International Research Trends in Competency-Based Education Using Topic Modeling

Education Sciences ◽

10.3390/educsci11060303 ◽

2021 ◽

Vol 11 (6) ◽

pp. 303

Author(s):

Seungsu Paek ◽

Taehun Um ◽

Namhyoung Kim

Keyword(s):

Education Reform ◽

International Research ◽

Topic Modeling ◽

Modern Society ◽

Research Trends ◽

Competency Development ◽

Competency Based Education ◽

Competency Based ◽

Latent Topics ◽

International Research Trends

Recently, there has been growing educational interest in competency. Global organizations, such as the United Nations (UN) and Organization for Economic Co-operation and Development (OECD), which are leading the discourse on education reform, are undertaking the lead in spreading awareness regarding competency education. Since 2015, the number of published articles on competency education has been rapidly increasing. This paper aims to provide significant implications for creating a sustainable future of competency education. A topic modeling method was used to empirically analyze latent topics and international research trends in 26,532 articles published on competency-based education (CBE). As a result of the analysis, 15 topics were derived, including “approach to competency development.” In addition, five topics including “learning skills” and “teacher training” were found to be hot topics with the increasing article publication. The rapidly changing modern society is calling for a transformation in education. We hope that the results of this study paves the way for further research exploring new directions for education, such as competency education.

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Analysis of Research Trends in Online Discussion Using Topic Modeling

Institute of Brain-based Education Korea National University of Education ◽

10.31216/bdl.20210028 ◽

2021 ◽

Vol 11 (3) ◽

Keyword(s):

Topic Modeling ◽

Online Discussion ◽

Research Trends

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A topic modeling analysis of Korea’s T&I research trends in the 2010s

Babel ◽

10.1075/babel.00228.lee ◽

2021 ◽

Author(s):

Changsoo Lee

Keyword(s):

Topic Modeling ◽

Research Output ◽

Research Trends ◽

Korean Language ◽

Modeling Analysis ◽

Trends Over Time ◽

New Research ◽

Topic Structure ◽

Manual Classification

Abstract The present study aims to demonstrate the relevance of topic modeling as a new research tool for analyzing research trends in the T&I field. Until now, most efforts to this end have relied on manual classification based on pre-established typologies. This method is time- and labor-consuming, prone to subjective biases, and limited in describing a vast amount of research output. As a key component of text mining, topic modeling offers an efficient way of summarizing topic structure and trends over time in a collection of documents while being able to describe the entire system without having to rely on sampling. As a case study, the present paper applies the technique to analyzing a collection of abstracts from four Korean Language T&I journals for the 2010s decade (from 2010 to 2019). The analysis proves the technique to be highly successful in uncovering hidden topical structure and trends in the abstract corpus. The results are discussed along with implications of the technique for the T&I field.

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