scholarly journals Understanding MOOC Reviews: Text Mining using Structural Topic Model

2021 ◽  
Author(s):  
Xieling Chen ◽  
Gary Cheng ◽  
Haoran Xie ◽  
Guanliang Chen ◽  
Di Zou
Keyword(s):  
Text Mining ◽  
Topic Model ◽  
Structural Topic Model

scholarly journals TRENDS AND FOUNDATIONS IN RESEARCH ON STUDENTS’ CONCEPTUAL UNDERSTANDING IN SCIENCE EDUCATION: A METHOD BASED ON THE STRUCTURAL TOPIC MODEL

2020 ◽  
Vol 19 (4) ◽  
pp. 551-568
Author(s):  
Shuaishuai Mi ◽  
Shanshan Lu ◽  
Hualin Bi
Keyword(s):  
Science Education ◽  
Regression Analysis ◽  
Text Mining ◽  
Conceptual Understanding ◽  
Topic Model ◽  
Research Interest ◽  
Mental Process ◽  
Journal Publication ◽  
Second Stage ◽  
Structural Topic Model

This study aims to reveal the trends and foundations in research on students’ conceptual understanding in science education. The literature was selected from three recognized journals in science education. The Structural Topic Model (STM) method was used to categorize articles into ten topics considering information about the semantic cohesion and exclusivity of words to topics. The topic, which has attracted increasing research interest, was selected using a method similar to standard regression analysis, and its changing focus was identified through an analysis of its research contents. Foundations of research about students’ conceptual understanding between 1980–1999 and 2000–2019 were obtained through a review of their top 10 most-cited papers. Three conclusions were drawn: a) there were ten sub-topics of research about students’ conceptual understanding; b) the research on the development (or pathways) of students’ scientific argumentation/reasoning is likely to attract further interest in the future; and c) compared to the studies in the first period, the studies in the second stage favor research on the description (nature, mental process, etc.) of the process of students’ conceptual understanding as the research foundation. Keywords: conceptual understanding, journal publication, structural topic model, text mining.

Research Topics and Trends in the Maritime Transport: a Structural Topic Model

2020 ◽  
Author(s):  
Xiwen Bai ◽  
Xiunian Zhang ◽  
Kevin X. Li ◽  
Yaoming Zhou ◽  
Kum Fai Yuen
Keyword(s):  
Topic Model ◽  
Maritime Transport ◽  
Research Topics ◽  
Structural Topic Model

scholarly journals The Voice of Drug Consumers: Online Textual Review Analysis Using Structural Topic Model

2020 ◽  
Vol 17 (10) ◽  
pp. 3648 ◽  
Author(s):  
Lifeng He ◽  
Dongmei Han ◽  
Xiaohang Zhou ◽  
Zheng Qu
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Web Based ◽  
Pharmaceutical Manufacturers ◽  
Textual Data ◽  
Consumer Dissatisfaction ◽  
The Voice ◽  
Document Level ◽  
Structural Topic Model ◽  
Over Time

Many web-based pharmaceutical e-commerce platforms allow consumers to post open-ended textual reviews based on their purchase experiences. Understanding the true voice of consumers by analyzing such a large amount of user-generated content is of great significance to pharmaceutical manufacturers and e-commerce websites. The aim of this paper is to automatically extract hidden topics from web-based drug reviews using the structural topic model (STM) to examine consumers’ concerns when they buy drugs online. The STM is a probabilistic extension of Latent Dirichlet Allocation (LDA), which allows the consolidation of document-level covariates. This innovation allows us to capture consumer dissatisfaction along with their dynamics over time. We extract 12 topics, and five of them are negative topics representing consumer dissatisfaction, whose appearances in the negative reviews are substantially higher than those in the positive reviews. We also come to the conclusion that the prevalence of these five negative topics has not decreased over time. Furthermore, our results reveal that the prevalence of price-related topics has decreased significantly in positive reviews, which indicates that low-price strategies are becoming less attractive to customers. To the best of our knowledge, our work is the first study using STM to analyze the unstructured textual data of drug reviews, which enhances the understanding of the aspects of drug consumer concerns and contributes to the research of pharmaceutical e-commerce literature.

scholarly journals News Organizations’ Selective Link Sharing as Gatekeeping : A Structural Topic Model Approach

2019 ◽  
Vol 1 (1) ◽  
pp. 45-78
Author(s):  
Chankyung Pak
Keyword(s):  
Social Media ◽  
Topic Model ◽  
News Stories ◽  
News Websites ◽  
News Organizations ◽  
Computational Data ◽  
Topic Distribution ◽  
Editorial Decisions ◽  
Structural Topic Model ◽  
Model Approach

Abstract To disseminate their stories efficiently via social media, news organizations make decisions that resemble traditional editorial decisions. However, the decisions for social media may deviate from traditional ones because they are often made outside the newsroom and guided by audience metrics. This study focuses on selective link sharing as quasi-gatekeeping on Twitter ‐ conditioning a link sharing decision about news content. It illustrates how selective link sharing resembles and deviates from gatekeeping for the publication of news stories. Using a computational data collection method and a machine learning technique called Structural Topic Model (STM), this study shows that selective link sharing generates a different topic distribution between news websites and Twitter and thus significantly revokes the specialty of news organizations. This finding implies that emergent logic, which governs news organizations’ decisions for social media, can undermine the provision of diverse news.

scholarly journals Best Paper Selection

2019 ◽  
Vol 28 (01) ◽  
pp. 179-180
Keyword(s):  
Breast Cancer ◽  
Mental Health ◽  
Text Mining ◽  
Drug Treatment ◽  
Topic Model ◽  
Web Based ◽  
Novel Approach ◽  
Health Communities ◽  
Model Approach

Abdellaoui R, Foulquié P, Texier N, Faviez C, Burgun A, Schück S. Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach. J Med Internet Res 2018;20(3):e85 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5874436/ Jones J, Pradhan M, Hosseini M, Kulanthaivel A, Hosseini M. Novel Approach to Cluster Patient-Generated Data Into Actionable Topics: Case Study of a Web-Based Breast Cancer. JMIR Med Inform 2018;6(4):e45 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6293240/ Park A, Conway M, Chen AT. Examining Thematic Similarity, Difference, and Membership in Three Online Mental Health Communities from Reddit: A Text Mining and Visualization Approach. Comput Human Behav 2018 Jan;78:98-112 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5810583/

scholarly journals Framing the Brahmaputra River hydropower development: different concerns in riparian and international media reporting

Water Policy ◽  
2017 ◽  
Vol 19 (3) ◽  
pp. 496-512 ◽  
Author(s):  
Hanchen Jiang ◽  
Maoshan Qiang ◽  
Peng Lin ◽  
Qi Wen ◽  
Bingqing Xia ◽  
...  
Keyword(s):  
News Media ◽  
Media Coverage ◽  
Water Policy ◽  
Topic Model ◽  
Climate Change Impacts ◽  
Brahmaputra River ◽  
News Reporting ◽  
News Reports ◽  
Technology And Economy ◽  
Structural Topic Model

Development of the Brahmaputra River, which links China, India and Bangladesh, has been hindered by significant challenges, particularly political challenges. News reports can mirror the perceptions of political actors, but are, owing to the complexity of the issue, complicated and unstructured. We present a comparative content analysis of the overall framing in news reports of the Brahmaputra River development from major English news media. A structural topic model is established to discover latent topics in the corpus of 1,569 news articles published in 34 countries or regions. We find that politics, including domestic and international politics, dominates the news narratives. Environmental issues, such as glacier status and climate change impacts, are secondarily discussed. Technology and economy issues are less frequently presented in the media coverage. Advantages of upstream countries and dependences of downstream countries are reflected in news reporting and explicitly emerge in the structural topic model. These findings and implications are important for promoting mutual understanding and cooperation among riparian countries in developing the Brahmaputra River. The proposed approach is expected to be widely used as a methodological strategy in future water policy studies.

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