Data Analytics has become an essential part of the Internet of Things (IoT), mainly text analytics-related applications, since they can be utilized to benefit educational institutions, consumers, and enterprises. Text Analytics is excessively used in Smart Education after the emerging technologies such as personal computers, tablets, and even smartphones transformed the education system and improved the teaching methods by helping the teachers to evaluate the students' performance or determine the degree of similarity between a lecturer’s and the students’ posts in the discussion forum, and by collecting the students’ feedback on the teaching method, in order to categorize each feedback into positive or negative, which will help the lecturers in optimizing their way of teaching. In this paper, we highlight the main components of IoT analytics, along with a comprehensive background of text analytics used techniques and applications. This paper provides a comprehensive survey and comparison of the leveraged IoT Text Analytics models and methods in Smart Education and many other applications.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Abirami, A. M., & Gayathri, V. (2017). A survey on sentiment analysis methods and approach. In 2016 Eighth International Conference on Advanced Computing (ICoAC) (pp. 72-76). IEEE.
Al-Ashmoery, Y., & Messoussi, R. (2015). Learning analytics system for assessing students' performance quality and text mining in online communication. Intelligent Systems and Computer Vision (ISCV).
Ali, F., El-Sappagh, S., & Kwak, D. (2019). Fuzzy ontology and LSTM-based text mining: A transportation network monitoring system for assisting travel. sensors.
Allahyari, M., Pouriyeh, S., Assef, M., Safaei, S., Trippe, E. D., Gutierrez, J. B., & Kochut, K. (2017). A Brief survey of text mining: Classification, clustering and extraction techniques. Computation and Language.
Allama, Z., & Dhunny, Z. A. (2019). On big data, artificial intelligence and smart cities. Cities - Elsevier, 89, 80–91.
Alzahrani, S. M. (2018). Development of IoT mining machine for Twitter sentiment analysis: Mining in the cloud and results on the mirror. 15th Learning and Technology Conference (L&T, (pp. 86–95). Jeddah.
Asthana, S., & Megahed, A. (2017). A recommendation system for proactive health monitoring using IoT and wearable technologies. IEEE International Conference on AI & Mobile Services (AIMS), (pp. 14–21). Honolulu.
Aung, K. Z., & Myo, N. N. (2017). Sentiment analysis of students' comment using lexicon based approach. 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS).
Bayhaqy, A., Sfenrianto, S., Nainggolan, K., & Kaburuan, E. R. (2018). Sentiment analysis about E-commerce from tweets using decision tree, K-Nearest Neighbor, and Naïve bayes. International Conference on Orange Technologies (ICOT). Nusa Dua, BALI, Indonesia.
Buenaño-Fernández, D., Villegas-Ch, W., & Luján-Mora, S. (2018). Using text mining to evaluate student interaction in virtual learning environments. IEEE World Engineering Education Conference (EDUNINE).
Chaturvedi, N., Toshniwal, D., & Parida, M. (2020). Harnessing social interactions on twitter for smart transportation using machine learning. In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 281-290). Springer, Cham.
Dastanwala, P. B., & Patel, V. (2016). A review on social audience identification on twitter using text mining methods. IEEE WiSPNET.
Dou, M., He, T., Yin, H., Zhou, X., Chen, Z., & Luo, B. (2015). Predicting passengers in public transportation using smart card data. In Australasian Database Conference (pp. 28-40). Springer, Cham
Fayyaz, Z., Ebrahimian, M., Nawara, D., Ibrahim, A., & Kashef, R. (2020). Recommendation systems: Algorithms, challenges, metrics, and business opportunities. Applied Science, 10(21), 7748.
Gomede, E., Gaffo, F., Briganó, G., De Barros, R., & Mendes, L. (2018). Application of computational intelligence to improve education in smart cities. Sensors, 18(1), 267.
Gonzalez, M., Viana-Barrero, J., & Acosta-Vargas, P. (2020). Text mining in smart cities to identify Urban events and public service problems. dvances in Artificial Intelligence, Software and Systems Engineering.
Gupta, N., Saeed, H., Jha, S., Chahande, M., & Pandey, S. (2017). IoT based health monitoring systems. 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS).
Hong, J., Suk, J., Hwang, H., Kim, D., Kim, K., & Jeong, Y. (2018). Text mining analysis of online consumer reviews on home IoT services.
Ittoo, A., Nguyen, L. M., & Bosch, A. v. (2016). Text analytics in industry: Challenges, desiderata and trends. Computers in Industry Elsavier, 78.
Kingsley, O., Arturo, A.-P., Camacho-Zuñiga, C., Nisrine, H., Nakamura, E. L., & al., e. (2020). Impact of students evaluation of teaching: a text analysis of the teachers qualities by gender. International Journal of Educational Technology in Higher Education, Heidelberg, 17(1).
Lim, C., & Maglio, P. (2018). Data-driven understanding of smart service systems through text mining. Service Science.
Maheswari, M. U., & Sathiaseelan, D. J. (2015). Text mining: survey on techniques and applications. International Journal of Science and Research (IJSR), 2319–7064.
Marjani, M., Nasaruddin, F., Gani, A., Karim, A., Hashem, I. A., Siddiqa, A., & Yaqoob, I. (2016). Big IoT data analytics: Architecture, opportunities, and open research challenges. IEEE Access.
Meena, R., & Bai, V. T. (2019). Study on Machine learning based Social Media and Sentiment analysis for medical data applications. Third International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC 2019).
Meena, R., & Bai, V. T. (2019). Study on Machine learning based Social Media and Sentiment analysis for medical data applications. Proceedings of the Third International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC 2019).
Murad, D. F., Heryadi, Y., Isa, S. M., Budiharto, W., & Wijanarko, B. D. (2018). Text Mining Analysis in the Log Discussion Forum for Online Learning Recommendation Systems. 2018 International Seminar on Research of Information Technology and Intelligent System.
Nair, P. C., Gupta, D., & Devi, B. I. (2020). A Survey of text mining approaches, techniques, and tools on discharge summaries. In Advances in Intelligent Systems and Computing book series (AISC, volume 1086) (pp. 331–348). Springer.
Nkomo, L. M., Ndukwe, I. G., & Daniel, B. K. (2020). Social network and sentiment analysis: Investigation of students’ perspectives on lecture recording. IEEE Access , 8.
Osman, A. M. (2019). A novel big data analytics framework for smart cities. Future Generation Computer Systems Elsavier, 91, 620–633.
Park, C., & Cho, S. (2017). Future sign detection in smart grids through text mining. Energy Procedia, (pp. 79–85).
Patel, P., Ali, M. I., & Sheth, A. (2017). On using the intelligent edge for iot analytics. IEEE Intelligent Systems , 32(5).
Pendyala, V. S., & Figueira, S. (2017). Automated medical diagnosis from clinical data. IEEE Third International Conference on Big Data Computing Service and Applications.
PraveenKumar, T. (2020). Exploring the students feelings and emotion towards online teaching: Sentimental analysis approach. International Working Conference on Transfer and Diffusion of IT.
Rahardja, U., Hariguna, T., & Baihaqi, W. M. (2019). Opinion mining on E-commerce data using sentiment analysis and K-Medoid clustering. 2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media).
Raji, A., Jeyasheeli, P. G., & Jenitha, T. (2016). IoT Based classification of vital signs data for chronic disease monitoring. 10th International Conference on Intelligent Systems and Control (ISCO).
Rangu, C., Chatterjee, S., & Valluru, S. (2017). Text mining approach for product quality enhancement: (Improving product quality through machine learning). IEEE International Advance Computing Conference, IACC.
Rani, S., & Kumar, P. (2017). A sentiment analysis system to improve teaching and learning. Computer, 50(5).
Rathi, M., Malik, A., Varshney, D., Sharma, R., & Mendiratta, S. (2018). Sentiment analysis of tweets using machine learning approach. Proceedings of 2018 Eleventh International Conference on Contemporary Computing.
Statista. (n.d.). Retrieved from https://www.statista.com/statistics/871513/worldwide-data-created/
Statista. (n.d.). Retrieved from https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/
Robinson, C., Yeomans, M., Reich, J., Hulleman, C., & Gehlbach, H. (2016). forecasting student achievement in MOOCs with natural language processing. ICPS Proceedings.
Rumi, R. I., Pavel, M. I., Islam, E., Shakir, M. B., & Hossain, M. A. (2019). IoT enabled prescription reading smart medicine dispenser implementing maximally stable extremal regions and OCR. Third International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC 2019).
Trinh, S., Nguyen, L., & Vo, M. (2017). Combining lexicon-based and learning-based methods for sentiment analysis for product reviews in Vietnamese language. International Conference on Computer and Information Science.
Ulloa, D., Saleiro, P., Rossetti, R. J., & Silva, E. R. (2016). Mining social media for open innovation in transportation systems. 19th International Conference on Intelligent Transportation Systems (ITSC).
Villegas-Ch, W., Román-Cañizares, M., & Palacios-Pacheco, X. (2020). Improvement of an online education model with the integration of machine learning and data analysis in an LMS. Applied Sciences, 10(15).
Wang, B., Gao, L., An, T., Meng, M., & Zhang, T. (2018). A method of educational news classification based on emotional dictionary. 2018 Chinese Control And Decision Conference (CCDC).
Wang, X., Yang, D., Wen, M., Koedinger, K., & Rosé, C. P. (2015). Investigating how student’s cognitive behavior in MOOC discussion forums affect learning gains. Conference on Educational Data Mining (EDM).
Xylogiannopoulos, K., Karampelas, P., & Alhajj, R. (2017). Text mining in unclean, noisy or scrambled datasets for digital forensics analytics. 2017 European Intelligence and Security Informatics Conference (EISIC), (pp. 76–83). Athens,.
Yıldırım, F. M., Kaya, A., Öztürk, S. N., & Kılınç, D. (2019). A real-world text classification application for an E-commerce Platform. 2019 Innovations in Intelligent Systems and Applications Conference (ASYU). Izmir, Turkey.
Yu, F., & Zheng, D. (2017). Education data mining: How to mine interactive Text in MOOCs using natural language process. The 12th International Conference on Computer Science & Education (ICCSE 2017).
Zaman, K., & Mamun, K. A. (2017). An evaluation of smartphone apps for preventive healthcare focusing on Cardiovascular Disease. 4th International Conference on Advances in Electrical Engineering (ICAEE),.
Zhang, M. (2020). E-commerce comment sentiment classification based on deep learning. IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA). Chengdu, China.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Mohammed, A.H.K., Jebamikyous, HH., Nawara, D. et al. IoT text analytics in smart education and beyond. J Comput High Educ (2021). https://doi.org/10.1007/s12528-021-09295-x
- Text mining
- Big data
- Sentiment analysis
- Data analytics
- Smart education