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BİR METİN MADENCİLİĞİ UYGULAMASI: VOSVIEWER

Year 2020, Volume: 8 Issue: 2, 344 - 354, 31.08.2020

Abstract

Geçmişten günümüze kadar veri ve veri yığınlarından anlamlı bilgilerin üretilme çabası artarak devam etmektedir. Büyük veriden anlamlı bilginin üretilme sürecinde araştırmacıların üzerinde durduğu yaklaşımlardan birisi olarak görselleştirme, son yıllarda oldukça ilgi toplamış ve bir çok farklı alanda yaygınlaşmaya başlamıştır. Durum böyle olunca, veri madenciliğinde karmaşık ilişkilerin ortaya çıkarıldığı bibliyometri analizi çalışmalarında da elde edilen verinin görselleştirilmesi ve görselleştirme yaklaşımlarının incelenmesi önemli bir konu haline gelmiştir. Bu çalışmada, bibliyometrik ağların görselleştirilmesinde kullanılan ve aynı zamanda metinler içerisindeki kelime ve kelime öbeklerinin ilişkisini ortaya çıkarmaya yarayan başka bir ifade ile metin madenciliği uygulaması olan VOSviewer programının kullanımı incelenecektir. Bu inceleme ile birlikte veri madenciliği kapsamında kullanımına yönelik bilgilendirici yönlendirmelere yer verilecektir.

References

  • Al, U., & Coştur, R. (2007). Türk Psikoloji Dergisi’nin bibliyometrik profili. Türk kütüphaneciliği, 21(2), 142-163.
  • Baker, R. S. J. D. (2010). Data mining for education. International encyclopedia of education, 7(3), 112-118.
  • Beck, J.E., Mostow, J. (2008). How who should practice: Using learning decomposition to evaluate the efficacy of different types of practice for different types of students. Proceedings of the 9th International Conference on Intelligent Tutoring Systems, 353-362.
  • Berry, M. J., & Linoff, G. S. (2004). Data mining techniques: for marketing, sales, and customer relationship management. John Wiley & Sons.
  • Blažun Vošner, H., Bobek, S., Sternad Zabukovšek, S., & Kokol, P. (2017). Openness and information technology: a bibliometric analysis of literature production. Kybernetes, 46(5), 750–766. doi:10.1108/k-10-2016-0292
  • Chiang, W. Y. (2018). Applying data mining for online CRM marketing strategy: An empirical case of coffee shop industry in Taiwan. British Food Journal, 120(3), 665-675.
  • Cunningham, S. J., & Holmes, G. (1999). Developing innovative applications in agriculture using data mining. In The proceedings of the Southeast Asia regional computer confederation conference (pp. 25-29).
  • Da Silva Nascimento, K.R., & Alencar, M.H. (2016). Management of risks in natural disasters: A systematic review of the literature on NATECH events. Journal of Loss Prevention in the Process Industries, 44, 347-359.
  • Delen, D., & Crossland, M. D. (2008). Seeding the survey and analysis of research literature with text mining. Expert Systems with Applications, 34(3), 1707-1720.
  • Doleck, T., & Lajoie, S. (2018). Social networking and academic performance: A review. Education and Information Technologies, 23(1), 435–465.
  • Garfield, E. (2009). From the science of science to Scientometrics visualizing the history of science with HistCite software. Journal of Informetrics, 3(3), 173-179.
  • Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
  • Karanikas, H., & Theodoulidis, B. (2002). Knowledge discovery in text and text mining software. Centre for Research in Information Management, Department of Computation.
  • Kobayashi, V. B., Mol, S. T., Berkers, H. A., Kismihók, G., & Den Hartog, D. N. (2018). Text mining in organizational research. Organizational research methods, 21(3), 733-765.
  • Koh, H. C., & Tan, G. (2011). Data mining applications in healthcare. Journal of healthcare information management, 19(2), 65.
  • Monkman, G. G., Kaiser, M. J., & Hyder, K. (2018). Text and data mining of social media to map wildlife recreation activity. Biological conservation, 228, 89-99.
  • Moro, S., Cortez, P., & Rita, P. (2015). Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation. Expert Systems with Applications, 42(3), 1314-1324.
  • Mucherino, A., Papajorgji, P., & Pardalos, P. M. (2009). Data mining in agriculture (Vol. 34). Springer Science & Business Media.
  • Perianes-Rodriguez, A., Waltman, L., & Van Eck, N.J. (2016). Constructing bibliometric networks: A comparison between full and fractional counting. Journal of Informetrics, 10(4), 1178-1195.
  • Rajesh, D. (2011). Application of spatial data mining for agriculture. International Journal of Computer Applications, 15(2), 7-9.
  • Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert systems with applications, 33(1), 135-146.
  • Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12-27.
  • Salloum, S. A., AlHamad, A. Q., Al-Emran, M., & Shaalan, K. (2018). A survey of Arabic text mining. In Intelligent Natural Language Processing: Trends and Applications (pp. 417-431). Springer, Cham.
  • Scherf, M., Epple, A., & Werner, T. (2005). The next generation of literature analysis: integration of genomic analysis into text mining. Briefings in bioinformatics, 6(3), 287-297.
  • Shaw, M. J., Subramaniam, C., Tan, G. W., & Welge, M. E. (2001). Knowledge management and data mining for marketing. Decision support systems, 31(1), 127-137.
  • Short, J. C., Broberg, J. C., Cogliser, C. C., & Brigham, K. H. (2010). Construct validation using computer-aided text analysis (CATA) an illustration using entrepreneurial orientation. Organizational Research Methods, 13(2), 320-347.
  • Singh, N., Hu, C., & Roehl, W. S. (2007). Text mining a decade of progress in hospitality human resource management research: Identifying emerging thematic development. International Journal of Hospitality Management, 26(1), 131-147.
  • Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C. (2018). Social media analytics–Challenges in topic discovery, data collection, and data preparation. International journal of information management, 39, 156-168.
  • Şimşek Gürsoy, U. T. (2009). Veri Madenciliği ve Bilgi Keşfi. Birinci Baskı, Pegem Akademi, Ankara.
  • Temizkan, S. P., Çiçek, D., & Özdemir, C. (2015). Bibliometric profile of articles published on health tourism. International Journal of Human Sciences, 12(2), 394-415.
  • Tomar, D., & Agarwal, S. (2013). A survey on Data Mining approaches for Healthcare. International Journal of Bio-Science and Bio-Technology, 5(5), 241-266.
  • Van Eck, N. J., Waltman, L., Dekker, R., & Van den Berg, J. (2008). An experimental comparison of bibliometric mapping techniques. In 10th international conference on science and technology indicators, Vienna.
  • Van Eck, N. J., & Waltman, L. (2007). VOS: A new method for visualizing similarities between objects. In Advances in data analysis (pp. 299-306). Springer, Berlin, Heidelberg.
  • Van Eck, N., & Waltman, L. (2009). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.
  • Van Eck, N.J., & Waltman, L. (2011). Text mining and visualization using VOSviewer. ISSI Newsletter, 7(3), 50-54. (paper, preprint, supplementary material)
  • Van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. In Measuring scholarly impact (pp. 285-320). Springer, Cham.
  • Van Eck, N.J., & Waltman, L. (2017). Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics, 111(2), 1053–1070.
  • Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K., Gopidi, R., Chang, J. F., & Hua, L. (2012). Data mining in healthcare and biomedicine: a survey of the literature. Journal of medical systems, 36(4), 2431-2448.
  • Yu, D., Wang, W., Zhang, W., & Zhang, S. (2018). A bibliometric analysis of research on multiple criteria decision making. Current Science, 114(4), 747-758.
  • Zhao Y, Karypis G (2005). “Topic-driven Clustering for Document Datasets.” In“Proceedings of the 2005 SIAM International Conference on Data Mining (SDM05),”pp. 358–369.
  • Zvereva, O. M., & Shams, S. R. (2018, October). Software Support for Team Engineering: Educational Case for IT Students. In 2018 IV International Conference on Information Technologies in Engineering Education (Inforino) (pp. 1-5). IEEE.
  • Waltman, L., Van Eck, N. J., & Noyons, E. C. M. (2010). A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, 4(4), 629–635.
  • Weiss S, Indurkhya N, Zhang T, Damerau F (2004). Text Mining: Predictive Methods for Analyzing Unstructured Information. Springer-Verlag. ISBN 0387954333.

A TEXT MINING APPLICATION: VOSVIEWER

Year 2020, Volume: 8 Issue: 2, 344 - 354, 31.08.2020

Abstract

From past to present, efforts to produce meaningful information from data and data stacks increasingly continue. Visualization, as one of the approaches emphasized by researchers in the process of producing meaningful information from big data, has gained great interest in recent years and has become widespread in many different fields. As such, the visualization of data obtained in bibliometric analysis studies revealing complex relationships in data mining and examination of visualization approaches have become an important issue. In this study, the use of VOSviewer program, being a text mining application, used to visualize bibliometric networks and also used to reveal the relationship between words and phrases in the texts, will be examined. This technical note will include informative directions for the use of data mining.

References

  • Al, U., & Coştur, R. (2007). Türk Psikoloji Dergisi’nin bibliyometrik profili. Türk kütüphaneciliği, 21(2), 142-163.
  • Baker, R. S. J. D. (2010). Data mining for education. International encyclopedia of education, 7(3), 112-118.
  • Beck, J.E., Mostow, J. (2008). How who should practice: Using learning decomposition to evaluate the efficacy of different types of practice for different types of students. Proceedings of the 9th International Conference on Intelligent Tutoring Systems, 353-362.
  • Berry, M. J., & Linoff, G. S. (2004). Data mining techniques: for marketing, sales, and customer relationship management. John Wiley & Sons.
  • Blažun Vošner, H., Bobek, S., Sternad Zabukovšek, S., & Kokol, P. (2017). Openness and information technology: a bibliometric analysis of literature production. Kybernetes, 46(5), 750–766. doi:10.1108/k-10-2016-0292
  • Chiang, W. Y. (2018). Applying data mining for online CRM marketing strategy: An empirical case of coffee shop industry in Taiwan. British Food Journal, 120(3), 665-675.
  • Cunningham, S. J., & Holmes, G. (1999). Developing innovative applications in agriculture using data mining. In The proceedings of the Southeast Asia regional computer confederation conference (pp. 25-29).
  • Da Silva Nascimento, K.R., & Alencar, M.H. (2016). Management of risks in natural disasters: A systematic review of the literature on NATECH events. Journal of Loss Prevention in the Process Industries, 44, 347-359.
  • Delen, D., & Crossland, M. D. (2008). Seeding the survey and analysis of research literature with text mining. Expert Systems with Applications, 34(3), 1707-1720.
  • Doleck, T., & Lajoie, S. (2018). Social networking and academic performance: A review. Education and Information Technologies, 23(1), 435–465.
  • Garfield, E. (2009). From the science of science to Scientometrics visualizing the history of science with HistCite software. Journal of Informetrics, 3(3), 173-179.
  • Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
  • Karanikas, H., & Theodoulidis, B. (2002). Knowledge discovery in text and text mining software. Centre for Research in Information Management, Department of Computation.
  • Kobayashi, V. B., Mol, S. T., Berkers, H. A., Kismihók, G., & Den Hartog, D. N. (2018). Text mining in organizational research. Organizational research methods, 21(3), 733-765.
  • Koh, H. C., & Tan, G. (2011). Data mining applications in healthcare. Journal of healthcare information management, 19(2), 65.
  • Monkman, G. G., Kaiser, M. J., & Hyder, K. (2018). Text and data mining of social media to map wildlife recreation activity. Biological conservation, 228, 89-99.
  • Moro, S., Cortez, P., & Rita, P. (2015). Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation. Expert Systems with Applications, 42(3), 1314-1324.
  • Mucherino, A., Papajorgji, P., & Pardalos, P. M. (2009). Data mining in agriculture (Vol. 34). Springer Science & Business Media.
  • Perianes-Rodriguez, A., Waltman, L., & Van Eck, N.J. (2016). Constructing bibliometric networks: A comparison between full and fractional counting. Journal of Informetrics, 10(4), 1178-1195.
  • Rajesh, D. (2011). Application of spatial data mining for agriculture. International Journal of Computer Applications, 15(2), 7-9.
  • Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert systems with applications, 33(1), 135-146.
  • Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12-27.
  • Salloum, S. A., AlHamad, A. Q., Al-Emran, M., & Shaalan, K. (2018). A survey of Arabic text mining. In Intelligent Natural Language Processing: Trends and Applications (pp. 417-431). Springer, Cham.
  • Scherf, M., Epple, A., & Werner, T. (2005). The next generation of literature analysis: integration of genomic analysis into text mining. Briefings in bioinformatics, 6(3), 287-297.
  • Shaw, M. J., Subramaniam, C., Tan, G. W., & Welge, M. E. (2001). Knowledge management and data mining for marketing. Decision support systems, 31(1), 127-137.
  • Short, J. C., Broberg, J. C., Cogliser, C. C., & Brigham, K. H. (2010). Construct validation using computer-aided text analysis (CATA) an illustration using entrepreneurial orientation. Organizational Research Methods, 13(2), 320-347.
  • Singh, N., Hu, C., & Roehl, W. S. (2007). Text mining a decade of progress in hospitality human resource management research: Identifying emerging thematic development. International Journal of Hospitality Management, 26(1), 131-147.
  • Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C. (2018). Social media analytics–Challenges in topic discovery, data collection, and data preparation. International journal of information management, 39, 156-168.
  • Şimşek Gürsoy, U. T. (2009). Veri Madenciliği ve Bilgi Keşfi. Birinci Baskı, Pegem Akademi, Ankara.
  • Temizkan, S. P., Çiçek, D., & Özdemir, C. (2015). Bibliometric profile of articles published on health tourism. International Journal of Human Sciences, 12(2), 394-415.
  • Tomar, D., & Agarwal, S. (2013). A survey on Data Mining approaches for Healthcare. International Journal of Bio-Science and Bio-Technology, 5(5), 241-266.
  • Van Eck, N. J., Waltman, L., Dekker, R., & Van den Berg, J. (2008). An experimental comparison of bibliometric mapping techniques. In 10th international conference on science and technology indicators, Vienna.
  • Van Eck, N. J., & Waltman, L. (2007). VOS: A new method for visualizing similarities between objects. In Advances in data analysis (pp. 299-306). Springer, Berlin, Heidelberg.
  • Van Eck, N., & Waltman, L. (2009). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.
  • Van Eck, N.J., & Waltman, L. (2011). Text mining and visualization using VOSviewer. ISSI Newsletter, 7(3), 50-54. (paper, preprint, supplementary material)
  • Van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. In Measuring scholarly impact (pp. 285-320). Springer, Cham.
  • Van Eck, N.J., & Waltman, L. (2017). Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics, 111(2), 1053–1070.
  • Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K., Gopidi, R., Chang, J. F., & Hua, L. (2012). Data mining in healthcare and biomedicine: a survey of the literature. Journal of medical systems, 36(4), 2431-2448.
  • Yu, D., Wang, W., Zhang, W., & Zhang, S. (2018). A bibliometric analysis of research on multiple criteria decision making. Current Science, 114(4), 747-758.
  • Zhao Y, Karypis G (2005). “Topic-driven Clustering for Document Datasets.” In“Proceedings of the 2005 SIAM International Conference on Data Mining (SDM05),”pp. 358–369.
  • Zvereva, O. M., & Shams, S. R. (2018, October). Software Support for Team Engineering: Educational Case for IT Students. In 2018 IV International Conference on Information Technologies in Engineering Education (Inforino) (pp. 1-5). IEEE.
  • Waltman, L., Van Eck, N. J., & Noyons, E. C. M. (2010). A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, 4(4), 629–635.
  • Weiss S, Indurkhya N, Zhang T, Damerau F (2004). Text Mining: Predictive Methods for Analyzing Unstructured Information. Springer-Verlag. ISBN 0387954333.
There are 43 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Murat Artsın 0000-0002-4975-0238

Publication Date August 31, 2020
Published in Issue Year 2020 Volume: 8 Issue: 2

Cite

APA Artsın, M. (2020). BİR METİN MADENCİLİĞİ UYGULAMASI: VOSVIEWER. Eskişehir Teknik Üniversitesi Bilim Ve Teknoloji Dergisi B - Teorik Bilimler, 8(2), 344-354.
AMA Artsın M. BİR METİN MADENCİLİĞİ UYGULAMASI: VOSVIEWER. Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler. August 2020;8(2):344-354.
Chicago Artsın, Murat. “BİR METİN MADENCİLİĞİ UYGULAMASI: VOSVIEWER”. Eskişehir Teknik Üniversitesi Bilim Ve Teknoloji Dergisi B - Teorik Bilimler 8, no. 2 (August 2020): 344-54.
EndNote Artsın M (August 1, 2020) BİR METİN MADENCİLİĞİ UYGULAMASI: VOSVIEWER. Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler 8 2 344–354.
IEEE M. Artsın, “BİR METİN MADENCİLİĞİ UYGULAMASI: VOSVIEWER”, Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler, vol. 8, no. 2, pp. 344–354, 2020.
ISNAD Artsın, Murat. “BİR METİN MADENCİLİĞİ UYGULAMASI: VOSVIEWER”. Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler 8/2 (August 2020), 344-354.
JAMA Artsın M. BİR METİN MADENCİLİĞİ UYGULAMASI: VOSVIEWER. Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler. 2020;8:344–354.
MLA Artsın, Murat. “BİR METİN MADENCİLİĞİ UYGULAMASI: VOSVIEWER”. Eskişehir Teknik Üniversitesi Bilim Ve Teknoloji Dergisi B - Teorik Bilimler, vol. 8, no. 2, 2020, pp. 344-5.
Vancouver Artsın M. BİR METİN MADENCİLİĞİ UYGULAMASI: VOSVIEWER. Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler. 2020;8(2):344-5.