Extracting Value from Unstructured Data – Implementing Text Analytics on the Voice of Student
DOI:
https://doi.org/10.14738/tmlai.84.8456Keywords:
Unstructured data, text mining, text analytics, student learningAbstract
Unstructured data is chaotic and messy with little or no metadata and lacks of traditional organization structure. However, same as any structured data, unstructured data is also part of valuable business asset. Many times, it is text heavy and needs extensive preprocessing before data mining algorithm can apply for building models in order to reveal value hidden in the data. Text as a form of data is widely used in business operations as a major way of communication, generating increasing volumes of data. Text data in its raw form is relatively dirty. The embedded business value can be extracted through approaches in text mining and text analytics. This paper presents a case study in this general process of revealing value in unstructured data and applying on data collected to support online learning and student assistance.
References
(1) Dang, S. and Ahmad, P. H. 2014. Text Mining: Techniques and Its Application. International Journal of Engineering & Technology Innovations (IJETI), Vol. 1, Issue 4, 22-25.
(2) Rybchak, Z. and Basystiuk, O. 2017. Analysis of Methods and Means of Text Mining. ECONTECHMOD: International Quarterly Journal on Economics of Technology and Modelling Processes, Vol. 6, No. 2, 73–78.
(3) Moreno, A. and Redondo, T. 2016. Text Analytics: the convergence of Big Data and Artificial Intelligence. International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 3, No. 6, 57-64.
(4) Talib, R., Hanif, M. K., Ayesha, S. and Fatima, F. 2016. Text Mining: Techniques, Applications and Issues. International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 7, No. 11, 2016.
(5) Ittoo A., Nguyen, L. M. and Bosch, A. 2016. Text analytics in industry: Challenges, desiderata and trends. Computers in Industry, Vol. 78, 96–107.
(6) Salloum, S. A., Al-Emran, M., Monem, A. A. and Shaalan K. 2017. A Survey of Text Mining in Social Media: Facebook and Twitter Perspectives. Advances in Science, Technology and Engineering Systems Journal, Vol. 2, No. 1, 127-133.
(7) Preethi, B. M. and Radha, P. 2017. A Survey Paper on Text Mining - Techniques, Applications and Issues. IOSR Journal of Computer Engineering (IOSR-JCE), e-ISSN: 2278-0661, p-ISSN: 2278-8727, 46-51.
(8) Khan, N., Alsaqer, M., Shah, H., Badsha, G., Abbasi, A. A. and Salehian, S. 2018. The 10 Vs, Issues and Challenges of Big Data. Pro¬ceedings of the 2018 International Conference on Big Data and Education, 52-56.
(9) Leung, C. K. 2015. Big Data Mining Applications and Services. Proceedings of the 2015 International Conference on Big Data Applications and Services, 1-8.
(10) Sinaeepourfard, A., Garcia, J., Masip-Bruin, X. and Marín-Torder, E. 2016. Towards a Comprehensive Data
LifeCycle Model for Big Data Environments. 2016 IEEE/ACM 3rd International Conference on Big Data Computing, Applications and Technologies, 100-106.
(11) Kourik, J. L. and Wang, J. 2017. The Intersection of Big Data and the Data Life Cycle: Impact on Data Management. International Journal of Knowledge Engineering (IJKE), Vol. 3, No. 2, 32-36.
(12) Jagadis, H.V., Gehreke, J., Labrinidis, A., Papakonstantinoue, Y., Patel, J. M., Ramakrishnan, R. and Shahabi, C. 2014. Big Data and Its Technical Challenges. Communications of the ACM, Vol. 57, No. 7, 86-94.
(13) Varudharajulu, A. K. and Ma, Y. 2018. Feature-based Restaurant Customer Reviews Process Model using Data Mining. Proceedings of the 2018 International Conference on Computing and Big Data, 32-37.
(14) Hu, M. and Liu, B. 2004. Mining and Summarizing Customer Reviews. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004), Seattle, Washington, USA.
