ANALYSIS OF EXTENDED REALITY PUBLCATONS IN INFORMATION SYSTEMS RESEARCH AREA THROUGH TEXT MINING AND NATURAL LANGUAGE PROCESSING (NLP) TECHNIQUES

Yıl 2022, Cilt: 4 Sayı: 3, 216 - 228, 01.10.2022

Tuncer Akbay

https://doi.org/10.47933/ijeir.1190214

Öz

The aim of the study is to cluster and to classify the scientific papers regarding Extended Reality indexed in Web of Science database. To achieve this goal, Extended Reality related publications were located and gathered from the database. NLTK library was used for tokenization, stop words removal, and lemmatization operations. The TF-IDF vectorizer method in the Sklearn library was used to convert words to vector format. Then, the keywords of the publications were clustered using K-Means. The keywords in each cluster were searched throughout the abstract of each publication. The publication was labeled as the name of the cluster wherein the largest number of keywords matches the words in its abstract. Then, Support Vector Classifier, and Multinomial Naïve Bayes machine learning algorithms and Gated Recurrent Unit deep learning algorithms were conducted for classification. The results of deep learning and machine learning have been compared and this comparison yielded that the dataset is more suitable for deep learning in comparison to machine learning. Accuracy values are reported as 90.4%, 77.2%, and 99.8% for Support Vector Classifier, Multinomial Naïve Bayes, and Gated Recurrent Unit respectively. This study provides evidence that the GRU architecture is more effective than the classical machine learning algorithms.

Anahtar Kelimeler

text mining, classification algorithms, gated recurrent unit, multinomial naïve bayes, extended reality, natural language processing

BİLİŞİM SİSTEMLERİ ARAŞTIRMA ALANINDAKİ GENİŞLETİLMİŞ GERÇEKLİK YAYINLARININ METİN MADENCİLİĞİ VE DOĞAL DİL İŞLEME (NLP) TEKNİKLERİ YOLUYLA ANALİZİ

Öz

Çalışmanın amacı, Web of Science veri tabanında indekslenen Genişletilmiş Gerçeklik ile ilgili bilimsel makaleleri kümelemek ve sınıflandırmaktır. Bu amaca ulaşmak için Genişletilmiş Gerçeklik ile ilgili yayınlar bulundu ve veri tabanından toplandı. Veri önişleme için NLTK kütüphanesi kullanılmıştır. Sözcükleri vektör formatına dönüştürmek için Sklearn kütüphanesindeki TF-IDF yöntemi kullanıldı. Daha sonra yayınların anahtar kelimeleri K-Means kullanılarak kümelenmiştir. Her bir kümedeki anahtar kelimeler, her yayının özeti boyunca arandı. Yayın, en fazla sayıda anahtar kelimenin özetindeki kelimelerle eşleştiği küme adı olarak etiketlendi. Ardından, Support Vector Classifier ve Multinomial Naive Bayes makine öğrenmesi algoritmaları ile Gated Recurrent Unit derin öğrenme algoritmaları sınıflandırma için gerçekleştirilmiştir. Derin öğrenme ve makine öğrenmesi sonuçları karşılaştırılmış ve bu karşılaştırma, veri setinin makine öğrenmesine kıyasla derin öğrenmeye daha uygun olduğunu ortaya koymuştur. Support Vector Classifier, Multinomial Naive Bayes ve Gated Recurrent Unit için doğruluk değerleri sırasıyla %90,4, %77,2 ve %99,8 olarak bildirilmiştir. Bu çalışma, GRU mimarisinin klasik makine öğrenmesi algoritmalarından daha etkili olduğuna dair kanıtlar sunmaktadır.

Anahtar Kelimeler

metin madenciliği, sınıflandırma algoritmaları, arttırılmış gerçeklik, sanal gerçeklik, doğal dil işleme, makine öğrenmesi, derin öğrenme

Kaynakça

• [1] Xi, N., & Hamari, J. (2021). Shopping in virtual reality: A literatüre review and future agenda. Journal of Business Research., 134, 37–58. https:// doi. org/ 10. 1016/j. jbusr es. 2021. 04. 075
• [2] Xi, N., Chen, J., Gama, F., Riar, M., & Hamari, J. (2022). The challenges of entering the metaverse: An experiment on the effect of extended reality on workload. Information Systems Frontiers, 1-22.
• [3] Kwok, A. O., & Koh, S. G. (2021). COVID-19 and extended reality (XR). Current Issues in Tourism., 24(14), 1935–1940. https://doi. org/ 10. 1080/ 13683 500. 2020. 17988 96
• [4] Milgram, P., & Kishino, F. (1994). A taxonomy of mixed reality visual displays. IEICE TRANSACTIONS on Information and Systems, 77(12), 1321-1329.
• [5] Milgram P, Takemura H, Utsumi A, Kishino F (1995). Augmented reality: a class of displays on the reality-virtuality continuum. In: Telemanipulator and telepresence technologies, vol 2351. International Society for Optics and Photonics, pp 282–292.
• [6] Wu, H., Lee, S. W., Chang, H., & Lian, J. (2013). Current status, opportunities and challenges of augmented reality in education. Computers & Education, 62, 41-49.
• [7] Kalawsky, R. S., Stedmon, A. W., Hill, K., & Cook, C. A. (2000). A taxonomy of technology: Defining augmented reality. SAGE, 44, 507-510.
• [8] Szalavári, Z., Schmalstieg, D., Fuhrmann, A., & Gervautz, M. (1998). “Studierstube”: An environment for collaboration in augmented reality. Virtual Reality, 3(1), 37-48.
• [9] Kesim, M., & Ozarslan, Y. (2012). Augmented reality in education: current technologies and the potential for education. Procedia - Social and Behavioral Sciences, 47, 297-302.
• [10] Alcañiz, M., Contero, M., Pérez-López, D. C., & Ortega, M. (2010). Augmented reality technology for education. In New Achievements in Technology, Education and Development. IntechOpen.
• [11] Farkas, M. G. (2010). Technology in practice. Your reality, augmented. American Libraries.
• [12] Arvanitis, T. N., Petrou, A., Knight, J. F., Savas, S., Sotiriou, S., Gargalakos, M., & Gialouri, E. (2009). Human factors and qualitative pedagogical evaluation of a mobile augmented reality system for science education used by learners with physical disabilities. Pers Ubiquit Comput, 13, 243-250.
• [13] Steuer, J. (1992). Defining virtual reality: Dimensions determining telepresence. Journal of communication, 42(4), 73-93.
• [14] Steffen, J. H., Gaskin, J. E., Meservy, T. O., Jenkins, J. L., & Wolman, I. (2019). Framework of affordances for virtual reality and augmented reality. Journal of Management Information Systems, 36(3), 683-729.
• [15] Slater, M., & Wilbur, S. (1997). A framework for immersive virtual environments (FIVE): Speculations on the role of presence in virtual environments. Presence: Teleoperators & Virtual Environments, 6(6), 603-616.
• [16] Azuma, R. T. (1997). A survey of augmented reality. Presence: teleoperators & virtual environments, 6(4), 355-385.
• [17] Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement. PLoS Med, 6(7), e1000097. http://dx.doi.org/10.1371/journal.pmed.1000097.
• [18] McAllister, J. T., Lennertz, L., & Atencio Mojica, Z. (2022). Mapping a discipline: a guide to using VOSviewer for bibliometric and visual analysis. Science & Technology Libraries, 41(3), 319-348.
• [19] He, H., Bai, Y., Garcia, E. A., & Li, S. (2008, June). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence) (pp. 1322-1328). IEEE.