scholarly journals Identifying Emerging Trends in Scientific Texts Using TF-IDF Algorithm: A Case Study of Medical Librarianship and Information Articles

2021 ◽  
Author(s):  
Meisam Dastani ◽  
Afshin Mousavi Chelak ◽  
Soraya Ziaei ◽  
Faeze Delghandi
Keyword(s):  
Text Mining ◽  
Information Technologies ◽  
Scientific Texts ◽  
Emerging Trends ◽  
Descriptive Approach ◽  
Python Programming ◽  
Mining Algorithms ◽  
Scientific Fields ◽  
Over Time

Background: Nowadays, due to the increased publication of articles in various scientific fields, identifying the publishing trends and emerging keywords in the texts of these articles is essential. Objectives: Thus, the present study identified and analyzed the keywords used in the published articles on medical librarianship and information. Methods: In the present investigation, an exploratory and descriptive approach was used to analyze librarianship and information articles published in specialized journals in this field from 1964 to 2019 by applying text mining techniques. The TF-IDF weighting algorithm was applied to identify the most important keywords used in the articles. The Python programming language was used to implement text mining algorithms. Results: The results obtained from the TF-IDF algorithm indicated that the words “Library”, “Patient”, and “Inform” with the weights of 95.087, 65.796, and 63.386, respectively, were the most important keywords in the published articles on medical librarianship and information. Also, the words “Catalog”, “Book”, and “Journal” were the most important keywords used in the articles published between the years 1960 and 1970, and the words “Patient”, “Bookstore”, and “Intervent” were the most important keywords used in articles on medical librarianship and information published from 2015 to 2020. The words “Blockchain”, “Telerehabilit”, “Instagram”, “WeChat”, and “Comic” were new keywords observed in articles on medical librarianship and information between 2015 and 2020. Conclusions: The results of the present study revealed that the keywords used in articles on medical librarianship and information were not consistent over time and have undergone a change at different periods so that nowadays, this field of science has also changed following the needs of society with the advent and growth of information technologies.

scholarly journals Topic Analysis of Published Articles in Medical librarianship and Information Science in Iran Using Text Mining Techniques

2020 ◽  
Vol 11 (4) ◽  
pp. 355-367 ◽  
Author(s):  
Meisam Dastani ◽  
Afshin Mousavi chelak ◽  
Soraya Ziaei ◽  
Faeze Delghandi
Keyword(s):  
Knowledge Management ◽  
Text Mining ◽  
Health Information ◽  
Information Literacy ◽  
Information Science ◽  
Publication Rates ◽  
Descriptive Approach ◽  
Python Programming ◽  
Mining Algorithms

Background and Objectives: Nowadays, due to the increasing publication of articles in various scientific fields, analysis of the topics published in specialized journals is interesting for researchers and practioners. For this purpose, this study has identified and analyzed the issues published in the Iranian library and medical librarianship articles. Material and Method: This study uses an exploratory and descriptive approach to analyze the library and information articles published in specialized journals in this field in Iran from 1997 to 2017 using text mining techniques. For this purpose, 982 articles on the library and medical librarianship have been selected from 16 journals. The TF-IDF weighting algorithm was used to identify the most important terms used in the articles and the LDA thematic modeling algorithm was used to determine the published topics. Python programming language has also been used to run text mining algorithms. Results: Results showed that the words of library (12.67), journal (12.47), information (12.23), hospital (9.90) and scientific (9.74) are the most important words based on their TF-IDF weight. The results of thematic modeling of these articles were based on the highest publication rates of scientometrics, information literacy, health information, knowledge management, webometrics, and the quality of the website and hospital information systems, respectively. Conclusion: The results of this study showed that the topics of scientometrics, information literacy and health information have had the highest publication in the last 5 years. Also, the publication of knowledge management, webometrics and quality of the website and hospital information system has been less published in the last 5 years than in the past.

scholarly journals SUPPORTING AIR TRANSPORT POLICIES USING BIG DATA ANALYTICS: A DESCRIPTIVE APPROACH BASED EMERGING TREND ANALYSIS

2017 ◽  
Vol 8 (1) ◽  
pp. 51-72
Author(s):  
Jin-seo Park
Keyword(s):  
Qualitative Research ◽  
Big Data ◽  
Text Mining ◽  
Research Methods ◽  
Qualitative Research Methods ◽  
Research Papers ◽  
Emerging Trends ◽  
The Future ◽  
Descriptive Approach ◽  
Core Issues

Qualitative research methods based on literature review or expert judgement have been used to find core issues, analyze emerging trends and discover promising areas for the future. Deriving results from large amounts of information under this approach is both costly and time consuming. Besides, there is a risk that the results may be influenced by the subjective opinion of experts. In order to make up for such weaknesses, the analysis paradigm for choosing future emerging trend is undergoing a shift toward mplementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The hange used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and the promising areas for the future from research papers pertaining to overall aviation areas through text mining method, which is one of the big data analysis techniques. This study has limitations in that its analysis for retrieving the aviation-related core issues and promising fields was restricted to research papers containing the keyword "aviation." However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and emerging trends regarding the promising areas for the future in the aviation industry through the application of a big data-based descriptive approach.

Quality of Use of a Complex Technology

2008 ◽  
pp. 947-964
Author(s):  
Marie-Claude Boudreau ◽  
Larry Sligman
Keyword(s):  
Information Technology ◽  
Informal Learning ◽  
Information Technologies ◽  
System Change ◽  
Future Research ◽  
Complex Information ◽  
Complex Technology ◽  
Over Time

It has been argued that simple conceptualizations of usage are inadequate for understanding and studying use of complex information technologies. In this paper we contend that quality of use, instead of the dichotomy of use versus non-use, is appropriate for understanding the extent to which a complex information technology is being used. An inductive case study of the implementation of a complex information technology was conducted, which led to the development of a learning-based model of quality of use. This model suggests the inclusion of factors relating to training (either formal or informal), learning, and beliefs, their impact on quality of use, and their change over time. Moreover, it describes how quality of use evolves over time as learning increases and perceptions of the system change. Evidence from the case study, along with relationships from the literature, is provided to support the model. Implications for future research are also discussed.

scholarly journals Strategies for Marketing Really New Products to the Mass Market: A Text Mining-Based Case Study of Virtual Reality Games

2019 ◽  
Vol 6 (1) ◽  
pp. 1 ◽  
Author(s):  
Jeffrey C. F. Ho ◽  
Xinzhi Zhang
Keyword(s):  
Virtual Reality ◽  
Text Mining ◽  
Text Analysis ◽  
Topic Modeling ◽  
New Products ◽  
Mass Market ◽  
The Third ◽  
Changes Over Time ◽  
Over Time

This paper reports on a text mining-based case study aimed at determining how virtual reality (VR) games, as examples of really new products (RNPs), market themselves when they are introduced to the mass market. The goal was to examine the marketing foci of RNPs and any subsequent changes over time when the RNPs survive. VR games are a type of RNP offering several unique benefits, such as immersive gameplay and storytelling, which are advanced compared with their earlier counterparts. To examine the marketing foci of VR games, we collected 17,000 pieces of promotional text from a major online gaming marketplace, Steam Store, published from the beginning of the second quarter of 2016 to the third quarter of 2018. We performed text analysis (topic modeling) and found that game marketers paid particular attention to the VR nature of VR games when they first entered the marketplace. However, game content increasingly was emphasized in subsequent quarters. In addition, the marketing foci for VR games seemed to go through an exploratory process, which was not observed among non-VR games in the same period. The results offer insights into how the focus of RNPs’ marketing evolves as their newness fades.

Retrospective and prospective approaches of coronavirus publications in the last half-century: a Latent Dirichlet allocation analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Farshid Danesh ◽  
Meisam Dastani ◽  
Mohammad Ghorbani
Keyword(s):  
Immune Response ◽  
Text Mining ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Statistical Population ◽  
Content Type ◽  
Number Of Publications ◽  
Python Programming ◽  
Mining Algorithms ◽  
Dirichlet Allocation

PurposeThe present article's primary purpose is the topic modeling of the global coronavirus publications in the last 50 years.Design/methodology/approachThe present study is applied research that has been conducted using text mining. The statistical population is the coronavirus publications that have been collected from the Web of Science Core Collection (1970–2020). The main keywords were extracted from the Medical Subject Heading browser to design the search strategy. Latent Dirichlet allocation and Python programming language were applied to analyze the data and implement the text mining algorithms of topic modeling.FindingsThe findings indicated that the SARS, science, protein, MERS, veterinary, cell, human, RNA, medicine and virology are the most important keywords in the global coronavirus publications. Also, eight important topics were identified in the global coronavirus publications by implementing the topic modeling algorithm. The highest number of publications were respectively on the following topics: “structure and proteomics,” “Cell signaling and immune response,” “clinical presentation and detection,” “Gene sequence and genomics,” “Diagnosis tests,” “vaccine and immune response and outbreak,” “Epidemiology and Transmission” and “gastrointestinal tissue.”Originality/valueThe originality of this article can be considered in three ways. First, text mining and Latent Dirichlet allocation were applied to analyzing coronavirus literature for the first time. Second, coronavirus is mentioned as a hot topic of research. Finally, in addition to the retrospective approaches to 50 years of data collection and analysis, the results can be exploited with prospective approaches to strategic planning and macro-policymaking.

Quality of Use of a Complex Technology

2011 ◽  
pp. 248-272 ◽  
Author(s):  
Marie-Claude Boudreau ◽  
Larry Sligman
Keyword(s):  
Information Technology ◽  
Informal Learning ◽  
Information Technologies ◽  
System Change ◽  
Future Research ◽  
Complex Information ◽  
Complex Technology ◽  
Over Time

It has been argued that simple conceptualizations of usage are inadequate for understanding and studying use of complex information technologies. In this paper we contend that quality of use, instead of the dichotomy of use versus non-use, is appropriate for understanding the extent to which a complex information technology is being used. An inductive case study of the implementation of a complex information technology was conducted, which led to the development of a learning-based model of quality of use. This model suggests the inclusion of factors relating to training (either formal or informal), learning, and beliefs, their impact on quality of use, and their change over time. Moreover, it describes how quality of use evolves over time as learning increases and perceptions of the system change. Evidence from the case study, along with relationships from the literature, is provided to support the model. Implications for future research are also discussed.

Using text mining algorithms in identifying emerging trends for recommender systems

2021 ◽  
Author(s):  
Iman Raeesi Vanani ◽  
Laya Mahmoudi ◽  
Seyed Mohammad Jafar Jalali ◽  
Kim-Hung Pho
Keyword(s):  
Text Mining ◽  
Recommender Systems ◽  
Emerging Trends ◽  
Mining Algorithms

When Culturally Significant Songs are Decontextualised: The Initiation Song Somagwaza, A Case Study

2018 ◽  
Vol 28 (2) ◽  
Author(s):  
T Dowling ◽  
Somikazi Deyi ◽  
Anele Gobodwana
Keyword(s):  
Qualitative Research ◽  
Comparative Method ◽  
Popular Song ◽  
Focus Group Discussions ◽  
Group Discussions ◽  
Ritual Music ◽  
Constant Comparative Method ◽  
Small Focus ◽  
Over Time

While there have been a number of studies on the decontextualisation and secularisation of traditional ritual music in America, Taiwan and other parts of the globe, very little has been written on the processes and transformations that South Africa’s indigenous ceremonial songs go through over time. This study was prompted by the authors’ interest in, and engagement with the Xhosa initiation song Somagwaza, which has been re-imagined as a popular song, but has also purportedly found its way into other religious spaces. In this article, we attempted to investigate the extent to which the song Somagwaza is still associated with the Xhosa initiation ritual and to analyse evidence of it being decontextualised and secularised in contemporary South Africa. Our methodology included an examination of the various academic treatments of the song, an analysis of the lyrics of a popular song, bearing the same name, holding small focus group discussions, and distributing questionnaires to speakers of isiXhosa on the topic of the song. The data gathered were analysed using the constant comparative method of analysing qualitative research.

Thinning in mature eastern white pine: 43-year case study

2002 ◽  
Vol 78 (4) ◽  
pp. 539-549 ◽  
Author(s):  
Paul D Anderson ◽  
John C Zasada ◽  
Glen W Erickson ◽  
Zigmond A Zasada
Keyword(s):  
Genetic Diversity ◽  
Dna Analysis ◽  
Large Diameter ◽  
White Pine ◽  
Thinning Intensity ◽  
Pine Regeneration ◽  
Volume Production ◽  
Pine Stands ◽  
Over Time

A white pine (Pinus strobus L.) stand at the western margin of the species range, approximately 125 years of age at present, was thinned in 1953 from 33.5 m2 ha-1 to target residual basal areas of 18.4, 23.0, 27.5, and 32.1 m2 ha-1 . Repeated measurement over the following 43-years indicated that the greatest total volume production and the greatest number of large diameter trees occurred in the unit of highest residual density. Over time, the distribution of stems was predominantly random although mortality between 1979 and 1996 resulted in a tendency for clumping in the 23.0 and 27.5 m2 ha-1 treatments. DNA analysis indicated that thinning intensity had little effect on the genetic diversity of residual white pine. This study suggests that mature white pine stands in northern Minnesota may be managed at relatively high densities without loss of productivity. However, regardless of overstory density, there was little or no white pine regeneration occurring in this stand. Key words: thinning, growth, genetic diversity, molecular markers, spatial pattern, regeneration

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