[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Research Article
BibTex RIS Cite

Year 2020, Volume: 3 Issue: 1, 31 - 48, 22.01.2020

Usame Ömer Osmanoğlu , Osman Nuri Atak , Kerim Çağlar , Hüseyin Kayhan , Talat Can

https://doi.org/10.31681/jetol.663733
Cited By: 16

Abstract

References

  • Akgul, E. S., Ertano, C., & Diri, B. (2016). Twitter verileri ile duygu analizi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 22(2), 106-110.
  • Altunisik, R. (2015). Big Data: Is it a Source of Opportunities or New Problems? Yildiz Social Science Review, 1(1), 45-76.
  • Artsın. M. (2018). Kitlesel Açık Çevrimiçi Derslerde Öğrenenlerin Öz-Yönetimli Öğrenme Becerilerinin İncelenmesi. Anadolu Üniversitesi, Eskişehir.
  • Baykara, M., & Gurturk, U. (2017). Sosyal Medya Paylaşımlarının Duygu Analizi Yöntemiyle Sınıflandırılması. In proceedings of 2. International Conferance on Computer Science and Engineering (pp. 911-916). Retrieved from http://web.firat.edu.tr/mbaykara/ubmk3.pdf
  • Boynukalın, Z. (2012). Emotion Analysis of Turkish Texts by Using Machine Learning Methods. Middle East Technical University. Retrieved from http://etd.lib.metu.edu.tr/upload/12618821/index.pdf
  • Bozkurt, A. (2016). Öğrenme analitiği: e-öğrenme, büyük veri ve bireyselleştirilmiş öğrenme. Açık Öğretim Uygulamaları ve Araştırmaları Dergisi (AUAd), 2(4), 55-81.
  • Bozkurt, A. (2019a). The historical development and adaptation of open universities in Turkish context: case of Anadolu university as a giga university. International Review of Research in Open and Distributed Learning, 20(4), 36-59. DOI: https://doi.org/10.19173/irrodl.v20i4.4086
  • Bozkurt, A. (2019b). From Distance Education to Open and Distance Learning: A Holistic Evaluation of History, Definitions, and Theories. In S. Sisman-Ugur, & G. Kurubacak (Eds.), Handbook of Research on Learning in the Age of Transhumanism (pp. 252-273). Hershey, PA: IGI Global. doi: https://doi.org/10.4018/978-1-5225-8431-5.ch016
  • Bozkurt, A. (2019c). Intellectual roots of distance education: a progressive knowledge domain analysis. Distance Education, 40(4), 497-514. DOI: https://doi.org/10.1080/01587919.2019.1681894
  • Bozkurt, A. (2019d). Vizyon 2023: Türkiye’de açık ve uzaktan öğrenme alanında somut ve soyut teknolojiler bağlamında eğilimler. Açık Öğretim Uygulamaları ve Araştırmaları Dergisi (AUAd), 5(4), 43-64. http://auad.anadolu.edu.tr/yonetim/icerik/makaleler/479-published.pdf
  • Bozkurt, A., & Hilbelink, A. (2019). Paradigm Shifts in Global Higher Education and e-learning: An ecological perspective. eLearn Magazine, 2019(5). DOI: https://doi.org/10.1145/3329488.3329487
  • Bozkurt, A., Keefer, J. (2017). Book Review: Knowing Knowledge. The European Journal of Open, Distance and E-Learning (EURODL). Retrieved from http://www.eurodl.org/materials/review/2017/Bozkurt_Keefer.pdf
  • Bozkurt, A., Zawacki-Richter, O., & Aydin, C. H. (2019). Using social network analysis to review the research in open and distance learning. In Proceedings of The Association for Educational Communications and Technology (AECT) 2019 International Convention (pp. 38-44). 21-25 October 2019, Las Vegas, NV. USA. Retrieved from https://members.aect.org/pdf/Proceedings/proceedings19/2019/19_06.pdf
  • Büyük, K., Kumtepe, A. T., Uça Güneş, E. P., Koçdar, S., Karadeniz, A., Özkeskin, E. … Öztürk A. (2018). Uzaktan öğrenenler ve öğrenme malzemesi tercihleri [Distance learners and their learning material preferences]. Eskişehir: Anadolu Üniversitesi. Retrieved from https://ekitap.anadolu.edu.tr/#bookdetail172074
  • Castells, M. (2004). The network society: A cross cultural perspective. MA, Northampton: Edward Elgar Publishing Limited.
  • Celik, O., & Altunaydın, S. S. (2018). A Research on Machine Learning Methods and Its Applications. Journal of Educational Technology and Online Learning, 1(3), 25-40. DOI: 10.31681\jetol.457046
  • Celik, O., & Aslan, A. F. (2019). Gender Prediction from Social Media Comments with Artificial Intelligence. Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 23(6), 1256-1264.
  • Celik, O., & Osmanoglu, U. O. (2019). Comparing to Techniques Used in Customer Churn Analysis. Journal of Multidisciplinary Developments, 4(1), 30-38.
  • Celik, O., & Osmanoglu, U. O. (2020). Sentiment Analysis from Social Media Comments. Mühendislik Bilimleri ve Tasarım Dergisi. 8(1).
  • Chatti, M. A., Jarke, M., & Quix, C. (2010). Connectivism: The network metaphor of learning. International Journal of Learning Technology, 5(1), 80-99.
  • Durahim, A. O., Coşkun Setırek, A., Başarır Özel, B., & Kebapci, H. (2018). Music emotion classification for Turkish songs using lyrics. Pamukkale University Journal of Engineering Sciences, 24(2).
  • Durak, G. (2017). Using social learning networks (SLNs) in higher education: Edmodo through the lenses of academics. The International Review of Research in Open and Distributed Learning, 18(1).
  • Durak, G., Çankaya, S., Yünkül, E., & Bozkurt, A. (2017). 5İ Derslerini Uzaktan Eğitimle Alan Öğrencilerin Görüşleri. VII. Uluslararası Eğitimde Araştırmalar Kongresi (s.89). 27-29 Nisan 2017, Çanakkale, Türkiye.
  • Düzenli, H., Özdamar, N., & Bozkurt, A. (2019). Examination of a distance education course through the lens of activity theory. In Proceedings of International Open & Distance Learning Conference (IODL19) (pp. 275-282). Anadolu University, Eskişehir, Turkey.
  • Garcia, S., & Yin, P. (2015). User Review Sentiment Classification and Aggregation. Retrieved from http://cs229.stanford.edu/proj2015/048_report.pdf
  • Gazioglu, K., & Seker, S. E. (2017). Veri Madenciliği Yöntemleri ile Twitter Üzerinden Girişimcilik Analizi. YBS Sözlük, 4(4), 1-6.
  • Gor, I. (2014). A Desing and Implementation of Geometrical Learning Algorithm for Vector Quantization. Adnan Menderes University, Department of Mathematics, Aydin.
  • Gunawardena, C. N., & McIsaac, M. S. (2013). Distance education. In Handbook of research on educational communications and technology (pp. 361-401). Routledge.
  • Guran, A., Uysal, M., & Dogrusoz, O. (2014). The Effect of Parameter Optimization on Support Vector Machines on Emotion Analysis. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 16(48), 86-93. Retrieved from https://dergipark.org.tr/tr/pub/deumffmd/issue/40797/492168
  • Kaynar, O., Gormez, Y., Yildiz, M., & Albayrak, A. (2016). Makine öğrenmesi yöntemleri ile Duygu Analizi. In Proceedings of International Artificial Intelligence and Data Processing Symposium (IDAP'16), September (pp. 17-18).
  • Oliveiar, L., & Figueira, A. (2017, April). Visualization of sentiment spread on social networked content: learning analytics for integrated learning environments. In 2017 IEEE Global Engineering Education Conference (EDUCON) (pp. 1290-1298). IEEE.
  • Parlar, T., Sarac, E., & Ozel, S. A. (2017, May). Comparison of feature selection methods for sentiment analysis on Turkish Twitter data. In 25th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE. doi: 10.1109/SIU.2017.7960388
  • Rainie, L., & Wellman, B. (2012). Networked: The new social operating system. MIT Press. Sebastian R. (2015). Python Machine Learning. Birmingham. UK: Packt Publishing, 2015. ISBN: 978-1783555130.
  • Şenocak, D. (2019). Açık ve uzaktan öğrenmede oyuncu tiplerinin motivasyon ve akademik başarı bağlamında incelenmesi. Yüksek lisans tezi. Anadolu Üniversitesi, Sosyal Bilimler Enstitüsü, Uzaktan Eğitim Anabilim Dalı. Eskişehir.
  • Sharma, R. C., Kawachi, P., & Bozkurt, A. (2019a). Exploring Changing Perspectives in Distance Education. Asian Journal of Distance Education, 14(1),1-6.
  • Sharma, R. C., Kawachi, P., & Bozkurt, A. (2019b). The landscape of artificial intelligence in open, online and distance education: Promises and concerns. Asian Journal of Distance Education, 14(2),1-2.
  • Shen, C. W., & Kuo, C. J. (2015). Learning in massive open online courses: Evidence from social media mining. Computers in Human Behavior, 51, 568-577.
  • Siemens, G. (2006). Knowing knowledge. Vancouver, BC, Canada: Lulu Press.
  • Siemens, G. (2012, April). Learning analytics: envisioning a research discipline and a domain of practice. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 4-8). ACM.
  • Sirmacek, B. (2007). Modelling a learning algorithm for a mobile robot with FPGA. Yildiz Technical University, Graduate School of Natural and Applied Sciences, Istanbul.
  • Turkmenoglu, C. (2016). Sentiment Analysis in Turkish Texts. Istanbul Technical University, Instıtute of Science and Technology, Istanbul.
  • Turkmenoglu, C., & Tantug, A. C. (2014, June). Sentiment analysis in Turkish media. In Proceedings of International Conference on Machine Learning (ICML). Retrieved from https://sentic.net/wisdom2014turkmenoglu.pdf
  • Uçar, H. & Kumtepe, A. T. (2018). Integrating Motivational Strategies into Massive Open Online Courses (MOOCs): The Application and Administration of the Motivation Design Model. In Administrative Leadership in Open and Distance Learning Programs (pp. 213-235). IGI Global.
  • Yigit, I. O. (2017). Çağrı Merkezi Metin Madenciliği Yazılım Çerçevesi. Retrieved from http://ceur-ws.org/Vol-1980/UYMS17_paper_3.pdf

Sentiment Analysis for Distance Education Course Materials: A Machine Learning Approach

Year 2020, Volume: 3 Issue: 1, 31 - 48, 22.01.2020

Usame Ömer Osmanoğlu , Osman Nuri Atak , Kerim Çağlar , Hüseyin Kayhan , Talat Can

https://doi.org/10.31681/jetol.663733
Cited By: 16

Abstract

Nowadays many companies and institutions are interested in learning what do people think and want. Many studies are conducted to answer these questions. That’s why, emotions of people are significant in terms of instructional design. However, processing and analysis of many people's ideas and emotions is a challenging task. That is where the 'sentiment analysis' through machine learning techniques steps in. Recently a fast digitalization process is witnessed. Anadolu university, that serves 1 million distant students, is trying to find its place in this digital era. A learning management system (LMS) that distant students of the Open Education Faculty (Açıköğretim Fakültesi) is developed at the Anadolu University.  Interaction with students is the clear advantage of LMS's when compared to the hard copy materials. Book, audio book (mp3), video and interactive tests are examples of these materials. 6059 feedbacks for those online materials was scaled using the triple Likert method and using machine learning techniques sentiment analysis was performed in this study. 0.775 correctness ratio was achieved via the Logistic regression algorithm. The research concludes that machine learning techniques can be used to better understand learners and how they feel.

Keywords

Emotion Analysis Machine Learning Application Feedbacks Deep Learning Estimation

References

  • Akgul, E. S., Ertano, C., & Diri, B. (2016). Twitter verileri ile duygu analizi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 22(2), 106-110.
  • Altunisik, R. (2015). Big Data: Is it a Source of Opportunities or New Problems? Yildiz Social Science Review, 1(1), 45-76.
  • Artsın. M. (2018). Kitlesel Açık Çevrimiçi Derslerde Öğrenenlerin Öz-Yönetimli Öğrenme Becerilerinin İncelenmesi. Anadolu Üniversitesi, Eskişehir.
  • Baykara, M., & Gurturk, U. (2017). Sosyal Medya Paylaşımlarının Duygu Analizi Yöntemiyle Sınıflandırılması. In proceedings of 2. International Conferance on Computer Science and Engineering (pp. 911-916). Retrieved from http://web.firat.edu.tr/mbaykara/ubmk3.pdf
  • Boynukalın, Z. (2012). Emotion Analysis of Turkish Texts by Using Machine Learning Methods. Middle East Technical University. Retrieved from http://etd.lib.metu.edu.tr/upload/12618821/index.pdf
  • Bozkurt, A. (2016). Öğrenme analitiği: e-öğrenme, büyük veri ve bireyselleştirilmiş öğrenme. Açık Öğretim Uygulamaları ve Araştırmaları Dergisi (AUAd), 2(4), 55-81.
  • Bozkurt, A. (2019a). The historical development and adaptation of open universities in Turkish context: case of Anadolu university as a giga university. International Review of Research in Open and Distributed Learning, 20(4), 36-59. DOI: https://doi.org/10.19173/irrodl.v20i4.4086
  • Bozkurt, A. (2019b). From Distance Education to Open and Distance Learning: A Holistic Evaluation of History, Definitions, and Theories. In S. Sisman-Ugur, & G. Kurubacak (Eds.), Handbook of Research on Learning in the Age of Transhumanism (pp. 252-273). Hershey, PA: IGI Global. doi: https://doi.org/10.4018/978-1-5225-8431-5.ch016
  • Bozkurt, A. (2019c). Intellectual roots of distance education: a progressive knowledge domain analysis. Distance Education, 40(4), 497-514. DOI: https://doi.org/10.1080/01587919.2019.1681894
  • Bozkurt, A. (2019d). Vizyon 2023: Türkiye’de açık ve uzaktan öğrenme alanında somut ve soyut teknolojiler bağlamında eğilimler. Açık Öğretim Uygulamaları ve Araştırmaları Dergisi (AUAd), 5(4), 43-64. http://auad.anadolu.edu.tr/yonetim/icerik/makaleler/479-published.pdf
  • Bozkurt, A., & Hilbelink, A. (2019). Paradigm Shifts in Global Higher Education and e-learning: An ecological perspective. eLearn Magazine, 2019(5). DOI: https://doi.org/10.1145/3329488.3329487
  • Bozkurt, A., Keefer, J. (2017). Book Review: Knowing Knowledge. The European Journal of Open, Distance and E-Learning (EURODL). Retrieved from http://www.eurodl.org/materials/review/2017/Bozkurt_Keefer.pdf
  • Bozkurt, A., Zawacki-Richter, O., & Aydin, C. H. (2019). Using social network analysis to review the research in open and distance learning. In Proceedings of The Association for Educational Communications and Technology (AECT) 2019 International Convention (pp. 38-44). 21-25 October 2019, Las Vegas, NV. USA. Retrieved from https://members.aect.org/pdf/Proceedings/proceedings19/2019/19_06.pdf
  • Büyük, K., Kumtepe, A. T., Uça Güneş, E. P., Koçdar, S., Karadeniz, A., Özkeskin, E. … Öztürk A. (2018). Uzaktan öğrenenler ve öğrenme malzemesi tercihleri [Distance learners and their learning material preferences]. Eskişehir: Anadolu Üniversitesi. Retrieved from https://ekitap.anadolu.edu.tr/#bookdetail172074
  • Castells, M. (2004). The network society: A cross cultural perspective. MA, Northampton: Edward Elgar Publishing Limited.
  • Celik, O., & Altunaydın, S. S. (2018). A Research on Machine Learning Methods and Its Applications. Journal of Educational Technology and Online Learning, 1(3), 25-40. DOI: 10.31681\jetol.457046
  • Celik, O., & Aslan, A. F. (2019). Gender Prediction from Social Media Comments with Artificial Intelligence. Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 23(6), 1256-1264.
  • Celik, O., & Osmanoglu, U. O. (2019). Comparing to Techniques Used in Customer Churn Analysis. Journal of Multidisciplinary Developments, 4(1), 30-38.
  • Celik, O., & Osmanoglu, U. O. (2020). Sentiment Analysis from Social Media Comments. Mühendislik Bilimleri ve Tasarım Dergisi. 8(1).
  • Chatti, M. A., Jarke, M., & Quix, C. (2010). Connectivism: The network metaphor of learning. International Journal of Learning Technology, 5(1), 80-99.
  • Durahim, A. O., Coşkun Setırek, A., Başarır Özel, B., & Kebapci, H. (2018). Music emotion classification for Turkish songs using lyrics. Pamukkale University Journal of Engineering Sciences, 24(2).
  • Durak, G. (2017). Using social learning networks (SLNs) in higher education: Edmodo through the lenses of academics. The International Review of Research in Open and Distributed Learning, 18(1).
  • Durak, G., Çankaya, S., Yünkül, E., & Bozkurt, A. (2017). 5İ Derslerini Uzaktan Eğitimle Alan Öğrencilerin Görüşleri. VII. Uluslararası Eğitimde Araştırmalar Kongresi (s.89). 27-29 Nisan 2017, Çanakkale, Türkiye.
  • Düzenli, H., Özdamar, N., & Bozkurt, A. (2019). Examination of a distance education course through the lens of activity theory. In Proceedings of International Open & Distance Learning Conference (IODL19) (pp. 275-282). Anadolu University, Eskişehir, Turkey.
  • Garcia, S., & Yin, P. (2015). User Review Sentiment Classification and Aggregation. Retrieved from http://cs229.stanford.edu/proj2015/048_report.pdf
  • Gazioglu, K., & Seker, S. E. (2017). Veri Madenciliği Yöntemleri ile Twitter Üzerinden Girişimcilik Analizi. YBS Sözlük, 4(4), 1-6.
  • Gor, I. (2014). A Desing and Implementation of Geometrical Learning Algorithm for Vector Quantization. Adnan Menderes University, Department of Mathematics, Aydin.
  • Gunawardena, C. N., & McIsaac, M. S. (2013). Distance education. In Handbook of research on educational communications and technology (pp. 361-401). Routledge.
  • Guran, A., Uysal, M., & Dogrusoz, O. (2014). The Effect of Parameter Optimization on Support Vector Machines on Emotion Analysis. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 16(48), 86-93. Retrieved from https://dergipark.org.tr/tr/pub/deumffmd/issue/40797/492168
  • Kaynar, O., Gormez, Y., Yildiz, M., & Albayrak, A. (2016). Makine öğrenmesi yöntemleri ile Duygu Analizi. In Proceedings of International Artificial Intelligence and Data Processing Symposium (IDAP'16), September (pp. 17-18).
  • Oliveiar, L., & Figueira, A. (2017, April). Visualization of sentiment spread on social networked content: learning analytics for integrated learning environments. In 2017 IEEE Global Engineering Education Conference (EDUCON) (pp. 1290-1298). IEEE.
  • Parlar, T., Sarac, E., & Ozel, S. A. (2017, May). Comparison of feature selection methods for sentiment analysis on Turkish Twitter data. In 25th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE. doi: 10.1109/SIU.2017.7960388
  • Rainie, L., & Wellman, B. (2012). Networked: The new social operating system. MIT Press. Sebastian R. (2015). Python Machine Learning. Birmingham. UK: Packt Publishing, 2015. ISBN: 978-1783555130.
  • Şenocak, D. (2019). Açık ve uzaktan öğrenmede oyuncu tiplerinin motivasyon ve akademik başarı bağlamında incelenmesi. Yüksek lisans tezi. Anadolu Üniversitesi, Sosyal Bilimler Enstitüsü, Uzaktan Eğitim Anabilim Dalı. Eskişehir.
  • Sharma, R. C., Kawachi, P., & Bozkurt, A. (2019a). Exploring Changing Perspectives in Distance Education. Asian Journal of Distance Education, 14(1),1-6.
  • Sharma, R. C., Kawachi, P., & Bozkurt, A. (2019b). The landscape of artificial intelligence in open, online and distance education: Promises and concerns. Asian Journal of Distance Education, 14(2),1-2.
  • Shen, C. W., & Kuo, C. J. (2015). Learning in massive open online courses: Evidence from social media mining. Computers in Human Behavior, 51, 568-577.
  • Siemens, G. (2006). Knowing knowledge. Vancouver, BC, Canada: Lulu Press.
  • Siemens, G. (2012, April). Learning analytics: envisioning a research discipline and a domain of practice. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 4-8). ACM.
  • Sirmacek, B. (2007). Modelling a learning algorithm for a mobile robot with FPGA. Yildiz Technical University, Graduate School of Natural and Applied Sciences, Istanbul.
  • Turkmenoglu, C. (2016). Sentiment Analysis in Turkish Texts. Istanbul Technical University, Instıtute of Science and Technology, Istanbul.
  • Turkmenoglu, C., & Tantug, A. C. (2014, June). Sentiment analysis in Turkish media. In Proceedings of International Conference on Machine Learning (ICML). Retrieved from https://sentic.net/wisdom2014turkmenoglu.pdf
  • Uçar, H. & Kumtepe, A. T. (2018). Integrating Motivational Strategies into Massive Open Online Courses (MOOCs): The Application and Administration of the Motivation Design Model. In Administrative Leadership in Open and Distance Learning Programs (pp. 213-235). IGI Global.
  • Yigit, I. O. (2017). Çağrı Merkezi Metin Madenciliği Yazılım Çerçevesi. Retrieved from http://ceur-ws.org/Vol-1980/UYMS17_paper_3.pdf
There are 44 citations in total.

Details

Primary Language English
Subjects Studies on Education
Journal Section Articles
Authors

Usame Ömer Osmanoğlu 0000-0002-1198-2447

Osman Nuri Atak 0000-0003-1559-1598

Kerim Çağlar 0000-0001-7675-4552

Hüseyin Kayhan 0000-0001-9036-3312

Talat Can 0000-0002-0220-3878

Publication Date January 22, 2020
Published in Issue Year 2020 Volume: 3 Issue: 1

Cite

APA Osmanoğlu, U. Ö., Atak, O. N., Çağlar, K., Kayhan, H., et al. (2020). Sentiment Analysis for Distance Education Course Materials: A Machine Learning Approach. Journal of Educational Technology and Online Learning, 3(1), 31-48. https://doi.org/10.31681/jetol.663733

Cited By

Investigation of Fluctuations in Cryptocurrency Transactions with Sentiment Analysis
Journal of Polytechnic
https://doi.org/10.2339/politeknik.1518826
Graph neural network based intelligent tutoring system: A survey
Neurocomputing
https://doi.org/10.1016/j.neucom.2024.128442
A new sentiment analysis model to classify students’ reviews on MOOCs
Education and Information Technologies
https://doi.org/10.1007/s10639-024-12526-0
Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers
Heliyon
https://doi.org/10.1016/j.heliyon.2023.e23784
ORDSAENet: Outlier Resilient Semantic Featured Deep Driven Sentiment Analysis Model for Education Domain
Journal of Machine and Computing
https://doi.org/10.53759/7669/jmc202303034
Emotions Matter: A Systematic Review and Meta-Analysis of the Detection and Classification of Students’ Emotions in STEM during Online Learning
Education Sciences
https://doi.org/10.3390/educsci13090914
E-learning experience: Modeling students’ e-learning interactions using log data
Journal of Educational Technology and Online Learning
https://doi.org/10.31681/jetol.938363
An Ensemble-Based Hotel Reviews System Using Naive Bayes Classifier
Computer Modeling in Engineering & Sciences
https://doi.org/10.32604/cmes.2023.026812
A machine learning-based approach for sentiment analysis on distance learning from Arabic Tweets
PeerJ Computer Science
https://doi.org/10.7717/peerj-cs.1047
Sentiment Analysis and Topic Mining Using a Novel Deep Attention-Based Parallel Dual-Channel Model for Online Course Reviews
Cognitive Computation
https://doi.org/10.1007/s12559-022-10083-7
Sentiment analysis and opinion mining on educational data: A survey
Natural Language Processing Journal
https://doi.org/10.1016/j.nlp.2022.100003
A U-Net Approach to Apical Lesion Segmentation on Panoramic Radiographs
BioMed Research International
https://doi.org/10.1155/2022/7035367
Estimation of renal scarring in children with lower urinary tract dysfunction by utilizing resampling technique and machine learning algorithms
Journal of Surgery and Medicine
Özer ÇELİK
https://doi.org/10.28982/josam.691768
Prediction of Survival and Recurrence Patterns by Machine Learning in Gastric Cancer Cases Undergoing Radiation Therapy and Chemotherapy
Advances in Radiation Oncology
Melek Akcay
https://doi.org/10.1016/j.adro.2020.07.007
DUYGU ANALİZİ VE FİKİR MADENCİLİĞİ UYGULAMALARI ÜZERİNE LİTERATÜR TARAMASI
Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi
Hatice Elif EKİM
https://doi.org/10.17780/ksujes.819367
https://doi.org/
 Download Cover Image


22029

JETOL is abstracted and indexed by ERIC - Education Resources Information Center.