Abstract
Every organization that interacts with some form of customers or users has a feedback system. Feedback provides a vital source of information to the organization about how the end users feel about their service. However, textual feedbacks are very subjective, and to be able to use them for rating or mathematical analysis, we need to quantify the textual feedback. Sentiment analysis can be performed on the textual feedback to attain a quantifiable output. In this paper we aim to design two systems; one that learns from raw text examples by clustering (unsupervised learning) the text samples and then assigning classes to these clusters, and another system that uses this trained data and classifiers to classify new textual data into one of the sentiment classes. For clustering we use K-means clustering method and discuss the performance of the same, and for classification we use two classifiers; K-nearest neighbors (KNN) and Naïve Bayes (NB). Finally, we compare the performance of the two proposed classifiers.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Shawver, Matthew A., Geoff J. Hanson, Greg J. Clark, Daniel D. Gilbertson, Aaron A. Kagawa, and Jian Shi Wang. “Design, Implementation, and Utilization of a Common Data Model for Vehicle Health Management.” In Aerospace Conference, 2007 IEEE, pp. 1–14. IEEE, 2007
Hripcsak, George, Jon D. Duke, Nigam H. Shah, Christian G. Reich, Vojtech Huser, Martijn J. Schuemie, Marc A. Suchard et al. “Observational Health Data Sciences and Informatics (OHDSI): opportunities for observational researchers.” Studies in health technology and informatics 216 (2015): 574.
Hanlin, Qin, Jin Xianzhen, and Zhang Xianrong. “Research on extract, transform and load (ETL) in land and resources star schema data warehouse.” In Computational Intelligence and Design (ISCID), 2012 Fifth International Symposium on, vol. 1, pp. 120–123. IEEE, 2012
Abadi, Daniel J., Peter A. Boncz, and Stavros Harizopoulos. “Column oriented database systems.” Proceedings of the VLDB Endowment 2, no. 2 (2009): 1664–1665
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Britto, J., Desai, K., Kothari, H., Ghane, S. (2020). Sentiment Analysis to Quantify Healthcare Data. In: Smys, S., Iliyasu, A.M., Bestak, R., Shi, F. (eds) New Trends in Computational Vision and Bio-inspired Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-41862-5_126
Download citation
DOI: https://doi.org/10.1007/978-3-030-41862-5_126
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-41861-8
Online ISBN: 978-3-030-41862-5
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)