The Role of Word and N-gram Frequency Analysis in Inference of the Content of Scientific Publication

• Autorzy: Iwona Zdonek
• Języki publikacji: EN

Abstrakty

EN

Purpose: The paper presents an analysis of a scientific publication with regard to the frequency of words and n-grams. The research problem addressed was the question to what extent the text mining analysis of a scientific publication will allow to infer its content. Design/methodology/approach: The main research method is the analysis of tokenized text using word count functions, bigrams, and trigrams in selected sections of a scientific publication. The results of text mining analysis were compared with the classic, non-automated text analysis of the publication. The presented study is a pilot project in the form of a case study. Findings: The proposed method of analyzing a scientific text using an analysis of the frequency of words and n-grams enables inference of the content of the paper with regard to the names of variables involved in the study, the statistical apparatus used and the key literature cited. It should be observed, however, that the discussed method does not make it possible to establish which variables are moderators and which are mediators. Originality/value: In this paper, the text mining technique was used differently in the discussed study than in previous works. The publication was not examined in its entirety, as previous researchers did, but text mining analysis was applied to individual parts of the paper, i.e. the part discussing theoretical foundations of the research and the part presenting the research method, research results, and their discussion. This allowed for obtaining more precise results regarding the content of the publication. (original abstract)

Słowa kluczowe

EN

Economic literature analysis; Text mining; Case study

PL

Analiza piśmiennictwa ekonomicznego; Eksploracja tekstu; Studium przypadku

• Czasopismo: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
• Rocznik: 2020
• Numer: z. 142 Quantitative Methods in Economics, Finance, Management and Quality Sciences
• Strony: 21--31

Twórcy

autor

Iwona Zdonek

• Silesian University of Technology

Bibliografia

• 1. Allahyari, M., and Kochut, K. (2015). Automatic Topic Labeling using Ontology-based Topic Models. Available online https://www.researchgate.net/publication/300408939_ Automatic_Topic_Labeling_Using_Ontology-Based_Topic_Models.
• 2. Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E.D, Gutierrez, J.B, and Kochut, K. (2017). A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques. Available online https://arxiv.org/pdf/1707.02919v2.pdf.
• 3. Berezina, K., Biligihan, A., Cobanoglu, C., and Okumus, F. (2016). Understanding Satisfied and Dissatisfied Hotel Customers: Text Mining of Online Hotel Reviews. Journal of Hospitality Marketing & Management, 25(1). doi: https://doi.org/10.1080/ 19368623.2015.983631.
• 4. Boussalis, C., and Coan, T.G. (2016). Text-mining the signals of climate change doubt. Global Environmental Change, 36, pp. 89-100. doi: https://doi.org/10.1016/j.gloenvcha. 2015.12.001.
• 5. Debortoli, S., Müller, O., Junglas, I., and vom Brocke, J. (2016). Text Mining For Information Systems Researchers: An Annotated Topic Modeling Tutorial. Communications of the Association for Information Systems, 39, doi: https://doi.org/ 10.17705/1CAIS.03907.
• 6. Fan, W., Wallace, L., Rich, S., and Zhang, Z. (2006). Tapping the power of text mining. Communications of the ACM, 49(9), pp. 76-82. Available online https://www.research gate.net/publication/220421836_Tapping_the_Power_of_Text_Mining.
• 7. Fleuren, W., and Alkema, W. (2015). Application of text mining in the biomedical domain. Methods, 74, pp. 97-106. doi: https://doi.org/10.1016/j.ymeth.2015.01.015.
• 8. Frawley, W., Piatetsky-Shapiro, G., and Matheus, C. (1992). Knowledge discovery in databases: An overview. AI Magazine, 13(3), pp. 57-70.
• 9. Hotho, A., Nürnberger, A., and Paaß, G. (2005). A Brief Survey of Text Mining. Available online https://www.researchgate.net/publication/215514577_A_Brief_Survey_of_Text_ Mining.
• 10. Kanat-Maymon, Y., Mor, Y., Gottlieb, E., and Anat Shoshani, A. (2017). Supervisor motivating styles and legitimacy: moderation and mediation models. Journal of Managerial Psychology. doi: https://doi.org/10.1108/JMP-01-2017-0043.
• 11. Krallinger, M., Rabal, O., Lourenço, A., Oyarzabal, J., and Valencia, A. (2017). Information Retrieval and Text Mining Technologies for Chemistry. Chemical Review, 117, 12, pp. 7673-7761, doi: https://doi.org/10.1021/acs.chemrev.6b00851.
• 12. Ngai, E.W.T., and Lee, P.T.Y. (2016). A review of the literature on applications of text mining in policy making. PACIS 2016 Proceedings. 343. Available online http://aisel.aisnet.org/pacis2016/343.
• 13. Silge, J., and Robinson, D. (2019). Text Mining with R. Available online https://www.tidytextmining.com/index.html.
• 14. Szymańska, A. (2017). Wykorzystanie algorytmów text mining do analizy danych tekstowych w psychologii. Socjolingwistyka, XXXI, pp. 99-116. Available online http://dx.doi.org./10.17651/SOCJOLING.31.6.
• 15. Vijayarani, S., Ilamathi, J., Nithya, and Phil, M. (2015). Preprocessing Techniques for Text Mining - An Overview. International Journal of Computer Science & Communi-cation Networks, 5(1), pp. 7-16. Available online https://pdfs.semanticscholar.org/1fa1/ 1c4de09b86a05062127c68a7662e3ba53251.pdf.
• 16. Wyskwarski, M. (2017). Text mining w analizie zbiorów publikacji naukowych. Zeszyty Naukowe Politechniki Śląskiej, seria Organizacja i Zarządzanie, 114. Avaliable online http://dx.doi.org/10.29119/1641-3466.2017.114.49.
• 17. Zwierzchowski, D. (2017). Text mining i narzędzia eksploracji tekstu. Available online https://www.researchgate.net/publication/313772976_Text_mining_i_narzedzia_eksplora cji_tekstu.

Identyfikatory

DOI

10.29119/1641-3466.2020.142.2