Agreeing to Disagree: Choosing Among Eight Topic-Modeling Methods

Fu, Qiang; Zhuang, Yufan; Gu, Jiaxin; Zhu, Yushu; Guo, Xin

doi:10.1016/j.bdr.2020.100173

Cited by 14 publications

(10 citation statements)

References 31 publications

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“…Through this approach, it is possible to understand what topics the keywords are composed of, the importance between keywords, and similar characteristics [ 24 , 25 ]. Since topic modeling has the advantage of considering and identifying all of the multiple topics included in a single document, it has recently become more widely used in studies related to the fields of management, policy, and industry [ 26 , 27 ].…”

Section: Methodsmentioning

confidence: 99%

Digital Health Care Industry Ecosystem: Network Analysis

Park¹,

Park²,

Lee³

2022

J Med Internet Res

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Background As the need for digital health care based on mobile devices is increasing, with the rapid development of digital technologies, especially in the face of the COVID-19 pandemic, gaining a better understanding of the industrial structure is needed to activate the use of digital health care. Objective The aim of this study was to suggest measures to revitalize the digital health care industry by deriving the stakeholders and major issues with respect to the ecosystem of the industry. Methods A total of 1822 newspaper articles were collected using Big Kings, a big data system for news, for a limited period from 2016 to August 2021, when the mobile health care project was promoted in Korea centered on public health centers. The R and NetMiner programs were used for network analysis. Results The Korean government and the Ministry of Health and Welfare showed the highest centrality and appeared as major stakeholders, and their common major issues were “reviewing the introduction of telemedicine,” “concerns about bankruptcy of local clinics,” and “building an integrated platform for precision medicine.” In addition, the major stakeholders of medical institutions and companies were Seoul National University Hospital, Kangbuk Samsung Hospital, Ajou University Hospital, Samsung, and Vuno Inc. Conclusions This analysis confirmed that the issues related to digital health care are largely composed of telemedicine, data, and health care business. For digital health care to develop as a national innovative growth engine and to be institutionalized, the development of a digital health care fee model that can improve the regulatory system and the cost-effectiveness of patient care, centering on the Ministry of Health and Welfare as a key stakeholder, is essential.

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Section: Methodsmentioning

confidence: 99%

Digital Health Care Industry Ecosystem: Network Analysis

Park¹,

Park²,

Lee³

2022

J Med Internet Res

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“…Topic consistency can be used to measure whether words within a topic are coherent, i.e., if a group of terms are consistent with each other, then these terms are coherent. For a specific topic, the semantic similarity between words in the topic determines the degree of coherence of the topic, so topic consistency can be measured by the semantic similarity between words in the topic [52][53][54][55]. The greater the consistency value of the topic is, the more coherent the words of each topic will be.…”

Section: Evaluation Methodsmentioning

confidence: 99%

Bert-Based Latent Semantic Analysis (Bert-LSA): A Case Study on Geospatial Data Technology and Application Trend Analysis

et al. 2021

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Geospatial data is an indispensable data resource for research and applications in many fields. The technologies and applications related to geospatial data are constantly advancing and updating, so identifying the technologies and applications among them will help foster and fund further innovation. Through topic analysis, new research hotspots can be discovered by understanding the whole development process of a topic. At present, the main methods to determine topics are peer review and bibliometrics, however they just review relevant literature or perform simple frequency analysis. This paper proposes a new topic discovery method, which combines a word embedding method, based on a pre-trained model, Bert, and a spherical k-means clustering algorithm, and applies the similarity between literature and topics to assign literature to different topics. The proposed method was applied to 266 pieces of literature related to geospatial data over the past five years. First, according to the number of publications, the trend analysis of technologies and applications related to geospatial data in several leading countries was conducted. Then, the consistency of the proposed method and the existing method PLSA (Probabilistic Latent Semantic Analysis) was evaluated by using two similar consistency evaluation indicators (i.e., U-Mass and NMPI). The results show that the method proposed in this paper can well reveal text content, determine development trends, and produce more coherent topics, and that the overall performance of Bert-LSA is better than PLSA using NPMI and U-Mass. This method is not limited to trend analysis using the data in this paper; it can also be used for the topic analysis of other types of texts.

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“…Surprisingly, conference papers comprised 53.2% (903) and articles comprised 40.1% (680). This is relevant because TM academic literature is positioned more in conference proceedings than in papers and only 2.06% (35) were reviews. All the variables from Scopus and WoS were included in this study; thus, the data analysis was sufficiently rigorous to understand the main contributions of TM.…”

Section: Metodology a Search Strategymentioning

confidence: 99%

Topic Modeling: Perspectives From a Literature Review

Grisales¹,

Robledo²,

Zuluaga

2023

IEEE Access

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Topic modeling is a Natural Language Processing technique that has gained popularity over the last ten years, with applications in multiple fields of knowledge. However, there is insufficient empirical evidence to show how this field of study has developed over the years, as well as the main models that have been applied in different contexts. The objective of this paper is to analyze the evolution of the topic modeling technique, the main areas in which it has been applied, and the models that are recommended for specific types of data. The methodology applied is based on bibliometric analysis. First, we searched the Web of Science and the Scopus databases. We then used scientometric techniques and a Tree of Science methodology, which allowed us to analyze the search results from the perspectives of classics, structure, and trends. The results show that the USA and China are among the most productive countries in this field and the applications have been mainly in the identification of sub-topics in short texts, such as social networks and blogs. The main conclusion of this work is that topic modeling is a versatile technique that can complement systematic literature reviews and that has been well-received in different academic and research contexts. The results of this study will help researchers and academics to recognize the importance of these techniques for reviewing large volumes of unstructured information, such as research articles, and in general, for systematic literature reviews.

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Agreeing to Disagree: Choosing Among Eight Topic-Modeling Methods

Cited by 14 publications

References 31 publications

Digital Health Care Industry Ecosystem: Network Analysis

Digital Health Care Industry Ecosystem: Network Analysis

Bert-Based Latent Semantic Analysis (Bert-LSA): A Case Study on Geospatial Data Technology and Application Trend Analysis

Topic Modeling: Perspectives From a Literature Review

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