Identifying the Thematic Relationships between the Resources Used By the Users of the Regional Science and Technology Information Center Using the Text Mining Technique
Subject Areas : Journal of Knowledge Studies
Khojasteh Shabani
1
(MA in information and library science, university of Isfahan.)
asefe asemi
2
(Faculty Member_Corvinus University of Budapest, Associate Professor of University of Isfahan.)
Keywords: Data mining, Text Mining, Co-word analysis, RICeST Database,
Abstract :
Objective: The main purpose of the present research was to investigate thematic relationships in the topics of resources used by RICeST users, using text mining techniques. Therefore, it has been attempted to reflect how the thematic relationships are in the information resources of users in the RICeST Center, in order to gain access to the required materials through understanding the behavior and feelings of users and clients.Methodology: The research method was based on text mining, which refers to data mining on the text, and text analysis in order to extract quality information from the text. Information access to the full text of articles in scientific-research, scientific-promotional journals, collections of scientific conference and conference articles, English and Persian books formed the statistical population of the research, and all the data obtained from the reporting by RICeST were checked using the census method. Data analysis and text analysis was done by Vianet software, and Python software was used to clean and normalize the data.Results: In order to determine the main view of the most used topics by RICeST users, based on the findings from the obtained data, 21 frequent words (used more than 2000 times in the RICeST database in a two-year interval 2018/02/08 – 2020/02/08).Conclusion: the conclusion was based on the fact that the compilation of the research in the collection of electronic resources of information databases and foresight in the future of this category of resources is useful to the managers of information centers and their users.
بتولی، ز. (1396). رابطه بین شاخص‌های پایگاه استنادی علوم و ریسرچ گیت: مطالعه موردی مقاله‌های داغ و پر استناد پژوهشگران ایرانی. پژوهشنامه پردازش و مدیریت اطلاعات، 33(161)، 183-191
رحیمی، م.، زاهدی، م.، و مشایخی، ه. (1397). یک مدل موضوعی احتمالاتی مبتنی بر روابط ملی واژگان در پنجره‌های هم پوشان. فصلنامه پردازش علائم و داده‌ها، (4)38، 57-70.
سلطانی، پ. و راستین، ف. (1379). دانشنامه کتابداری و اطلاع رسانی. فرهنگ معاصر.
سهرابی، ط. و غفاری، س. (1398). شناسایی موضوعات پر کاربرد تولیدات علمی حوزه ارتباطات علمی با استفاده از روش هم رخدادی واژگان. دو فصلنامه علمی دانشگاه شاهد، (2)5، 45-61.
سهیلی، ف.، خاصه، ع.، و کرانیان، پ. (1397). روند موضوعی مفاهیم حوزه علم اطلاعات و دانش شناسی ایران براساس تحلیل هم رخدادی واژگان. فصلنامه مطالعات ملی کتابداری و سازماندهی اطلاعات، (2)29، 172-190.
صدیقی، م. (1393). بررسی کاربرد روش تحلیل هم رخدادی واژگان در ترسیم ساختار حوزه‌های علمی (مطالعه موردی: حوزه اطلاع سنجی). پژوهشنامه پردازش و مدیریت اطلاعات، (2)30، 373-396.
کشاورزیان، س. و براردخت، ح. (1396). جایگاه کتاب و کتابخوانی در سایت تبیان با رویکرد متن کاوی و تحلیل شبکه‌های اجتماعی. فصلنامه مدیریت کسب و کار هوشمند، (21)6، 169-188.
متن کاوی (Text Mining) به زبان ساده (1398)، بازیابی شده در 12 فروردین 1399، از https://blog.faradars.org/introduction-to-text-mining/.
مرکز منطقه‌ای اطلاع رسانی علوم و فناوری (رایسست). (بی تا). تاریخچه مرکز منطقه‌ای اطلاع رسانی علوم و فناوری (رایسست). بازیابی شده در 17 تیر 1399، از https://ricest.ac.ir/ricest-history/.
مسعودی، ب. و راحتی قوچانی، س.(1394). رفع ابهام معنایی واژگان مبهم فارسی با مدل موضوعی LDA.. فصلنامه پردازش علائم و داده‌ها، (4)26، 117- 125.
مقدسی، ح.، حسینی، ا.، اسدی، ف.، و جهانبخش، م.(1391). داده کاوی و کاربرد آن در سلامت. مدیریت اطلاعات سلامت، (2)9، 297- 304.
_||_Allahyari, M., Pourieh, A., Assefi, M., Safaei, S., Trippe, J.B., Gutierrez, E., & Kochut, k. (2017). A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques. E-Printes, KDD Bigdas.
Asemi, A. (2020). Unstructured Data Analysis Recommender System (RSS) (text analysis by voyant). Corvinus university of Budapest.
Batuli, Z. (2017). The relationship between science citation database indicators and research Gate: a case study of hot and highly cited articles by Iranian researcher. Information Processig and Management, 33(161), 91-183. [In Persian].
Chen, H., Wang, X., Pan, s., xiong, F. (2019). Identify Topic Relation In Scientific Literature Using Topic Modeling. IEEE Transactions on Engineering Management, 1-13.
Garousi, V. & Mantyla, M.V. (2016). Citations, Research Topics and Active Countries in Software Engineering: Bibliometric study. Computer Science Review, 19(2), 56- 77.
Gholamhosseini, L. & Damarvi, M. (2015). Examining the applications of data mining in the health system. Journal of Paramedical Sciences and Military Health, 10(1), 39-48. [In Persian].
https://blog.faradars.org/introduction-to-text-mining/. [In Persian].
Ja-hyun, P. & Min, S. (2013). A Study on the Research Trends in Library & Information Science in Korean Using Topic Modeling. Journal of the Korean Society for Information Management, 30(1), 7-32.
Keshavarzian, S. & Barardokht, H. (2017). The position of books and reading on the tebian site with the approach of text mining and social network analysis. Smart Business Management Quarterly, 6(21), 169-188. [In Persian].
Leydesdorff, L., & Nerghes, A. (2016). Co-Word Maps And Topic Modeling: A Comparison Using Small And Medium- Sized Corpora (N<1000). Journal of the Association for Information Science and Technology.
Mantyla, M., Graziotin, & D., Kutila, M. (2018). The Evolution of Sentiment Analysis- A Review of Research Topics, Venues, and Top Cited Papers. Computer Science Review, 16-32.
Masudi, M., & Rahati Ghuchani, S. (2015). Resolving the semantic ambiguity of ambiguous Persian words with thematic model LDA. Quarterly Journal of Signal and Data Processing, 26(4), 117-125. [In Persian].
Miller, A. (2018). Text Mining Digital Humanities Projects: Assessing Content Analysis Capabilities of Voyant Tools. Journal of Web Librarianship, 12(3), 169-197.
Moghadassi, H., Hosseini, A., Asadi, F., & Jahanbakhshm M. (2012). Data mining and its application in health. Health Information Management, 9(2), 297-304. [In Persian].
Ogrady, W., Dobrovolsky, M., Aronoff, M. (1993). Contemporary Linguistics, an Introduction, 2nd Ed, St. Martin Press, INC.
Rahimi, M., Zahedi, M., & Mashayekhi, H. (2017). A probabilistic topic model based on national vocabulary relations in overlapping windows. Quarterly journal of signal and data processing, 38(4), 57-70. [In Persian].
Regional science and technology information center (RICEST) (without data). History Regional science and technology information center (RICEST). Retrieved 8 July 2020 https://ricest.ac.ir/ricest-history/. [In Persian].
Scott, M. & Tribble, C. (2006). Textual Patterns: Keyword and Corpus Analysis in Language Education. Benjamins.
Seddighi, M. (2014). Investigating the application of vocabulary co-occurrence analysis method in drawing the structure of scientific fields (Case study: field of scientometrics information). Information Processing And Management Research Paper, 30(2), 373-396. [In Persian].
Soheili, F., Khaseh, A., & Karanian, P. (2018). Thematic trend of concepts in the field of information science and epistemology of Iran based on co-occurrence analysis of words. Quarterly Journal of National Library Studies and Information Organization, 29(2), 172-190. [In Persian].
Sohrabi, T., & Ghafari, S. (2019). Identifying the frequently used topic of scientific productions in the field of scientific communication using the co-occurrence method of words. Two Scientific Quarterly Journals of Shahed University, 5(2), 45-61. [In Persian].
Soltani, P., & Rastin, F. (2000). Encyclopedia of librarianship and information. contemporary culture. [In Persian].
Talib, R., Kashif, M., Ayesha, S., & Fatima, F. (2016). Text Mining: Techniques, Applications and Issues. International Journal of Advanced Computer Science and Application, 4(3), 56-78.
Vorontsov, K., Frei, O., Romov, P., & Dudarenko, M. (2015). Open Source Library For Regularized Multimodal Conference On Analysis Of Images. Social Network and Texts, 370-381.
Zhou, X., Liu, B., Wu, Z. & Feng, Y. (2007). Integrative Mining of Traditional Chinese Medicine Literature and Medline for Functional Gene Networks. Artificial Intelligence in Medicine, 41(2), 87-104.