Application of Text Mining Techniques on Scholarly Research Articles: Methods and Tools

Thakur, Khusbu; Kumar, Vinit

doi:10.1080/13614533.2021.1918190

Cited by 24 publications

(20 citation statements)

References 89 publications

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“…To identify LIS research topics, researchers have traditionally used content analysis (Armann‐Keown & Patterson, 2020). Bibliometric methods (Ellegaard, 2018) and text mining (Thakur & Kumar, 2021) have also been applied to detect emerging research topics. Given that several characteristics of LIS research embedded in the content of articles are the research focus, this study used content analysis and only reviewed studies that also used content analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Disciplinary contributions to research topics and methodology in Library and Information Science—Leading to fragmentation?

Vakkari

Chang

Järvelin

2022

Asso for Info Science & Tech

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The study analyses contributions to Library and Information Science (LIS) by researchers representing various disciplines. How are such contributions associated with the choice of research topics and methodology? The study employs a quantitative content analysis of articles published in 31 scholarly LIS journals in 2015. Each article is seen as a contribution to LIS by the authors' disciplines, which are inferred from their affiliations. The unit of analysis is the article‐discipline pair. Of the contribution instances, the share of LIS is one third. Computer Science contributes one fifth and Business and Economics one sixth. The latter disciplines dominate the contributions in information retrieval, information seeking, and scientific communication indicating strong influences in LIS. Correspondence analysis reveals three clusters of research, one focusing on traditional LIS with contributions from LIS and Humanities and survey‐type research; another on information retrieval with contributions from Computer Science and experimental research; and the third on scientific communication with contributions from Natural Sciences and Medicine and citation analytic research. The strong differentiation of scholarly contributions in LIS hints to the fragmentation of LIS as a discipline.

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Section: Literature Reviewmentioning

confidence: 99%

Disciplinary contributions to research topics and methodology in Library and Information Science—Leading to fragmentation?

Vakkari

Chang

Järvelin

2022

Asso for Info Science & Tech

View full text Add to dashboard Cite

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“…Text mining is also used to get helpful information from a text which does not have a structured format (Prabowo & Wiguna, 2021). In this case, text mining is used as a type of Text mining algorithm to classify text on Twitter (Thakur & Kumar, 2022).…”

Section: Methodsmentioning

confidence: 99%

Sentiments via #Abrahamaccords on the UAE and Israel Normalization

Fikrie

Pradana

Suhermanto

2022

JKG

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On 15 September 2020, the UAE normalized relations with Israel, which sparked controversy on Twitter with the #AbrahamAccords. By using Digital Movement Opinion (DMO) and the concept of sentiment analysis, this study aims to determine the sentiment of public opinion that develops on Twitter media related to the normalization of this relationship. This study used a qualitative approach through the use of text mining methods using Natural language Toolkit (NLTK) as a platform in Python to analyze 4900 tweets with #AbrahamAccords. The results of the study showed that the sentiment of public opinion that developed on social media Twitter was a positive sentiment with 75% of the 490 tweets. It also showed that views on the relationship between the UAE and Israel on social media Twitter via #AbrahamAccords tend to support this normalization. Some factors that influenced the positive sentiment were the role of the mass media and political actors. Pada 15 September 2020, UEA melakukan normalisasi hubungan dengan Israel yang yang mengundang kontroversi di Twitter dengan #AbrahamAccords. Dengan menggunakan Digital Movement Opinion (DMO) dan konsep sentiment analisis, penelitian ini bertujuan untuk mengetahui sentimen dari opini publik yang berkembang pada media Twitter terkait dengan normalisasi hubungan tersebut. Penelitian ini menggunakan metode kualitatif melalui pemakaian metode teks mining dengan menggunakan Natural language Toolkit (NLTK) sebagai platform di Python guna menganalisis 4900 tweets dengan #AbrahamAccords. Hasil penelitian menunjukkan bahwa sentimen dari opini publik yang berkembang di media sosial Twitter adalah sentimen positif dengan 75% dari 490 tweet yang di peroleh. Hal ini menunjukkan bahwa pandangan terhadap hubungan UEA dengan Israel pada media sosial Twitter melalui #AbrahamAccords cenderung mendukung adanya normalisasi tersebut. Beberapa faktor yang mempengaruhi sentiment positif yang terjadi ialah peran media massa dan aktor politik.

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“…A limitation is that bibliometric indicators can be misinterpreted and conclusions drawn that the approach was not intended to measure [ 28 ]. Recent uses of computational methods using topic modelling may allow researchers to classify the substantive focus of a large number of research outputs and model relationships within research areas [ 29 ]. However, such methods still require domain specific knowledge to interpret classifications and may recover classifications that provide little insight into the review’s primary research questions [ 30 ].…”

Section: Main Textmentioning

confidence: 99%

Leveling up evidence syntheses: filling conceptual gaps of the role of midwifery in health systems through a network analysis

2022

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Objective In the research note, our main objective is to explore the value of combining an evidence synthesis with a network analysis. The discussion is based on a critical interpretive synthesis, which combines systematic review methodology with qualitive inquiry, and ‘research concept’ network analysis focused on understanding the roles of midwives in health systems. The interpretative analytic approach of a critical interpretive synthesis has a high explanatory value by allowing for the review of a diverse body of literature and is well-suited to delving into areas that are not well understood, such as midwifery. Results Network analyses use graphs to represent relationships between concepts and brought to light important additional insights into the literature that were not present in the evidence synthesis alone. Given the lack of theoretical development in the area of midwifery in health systems, the critical interpretive synthesis allowed for the generation of concepts used to inform a theoretical framework, while the novel application of an exploratory network analysis deepened understanding of conceptual areas of saturation within the field, as well as identifying critical gaps in the literature.

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Application of Text Mining Techniques on Scholarly Research Articles: Methods and Tools

Cited by 24 publications

References 89 publications

Disciplinary contributions to research topics and methodology in Library and Information Science—Leading to fragmentation?

Disciplinary contributions to research topics and methodology in Library and Information Science—Leading to fragmentation?

Sentiments via #Abrahamaccords on the UAE and Israel Normalization

Leveling up evidence syntheses: filling conceptual gaps of the role of midwifery in health systems through a network analysis

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