Abstract
Sentiment analysis is considered as a classification task as it classifies the polarity of a text into positive or negative. Different methods of sentiment analysis can be applied for the health domain, especially for prediabetes domain which has not been completely explored yet. And there is a lack of approaches for analyzing positive and negative tweets separately to identify the positive and negative ontologies for modeling the features in a domain of interest. Here in my work, proposed domain and sub-domains are Health and Prediabetes, respectively. Prediabetes defines the condition of blood sugar levels that are higher than normal but not high enough to be diabetes like a pre-warning call for diabetes. The proposed methodology is the deployment of original ontology-based techniques toward a more efficient sentiment analysis of Twitter posts on prediabetes. As part of experimentation, sentiment analysis uses the SVM algorithm with term frequency as a feature extraction method to train and test a large and sub-data set of tweet text. Negative ontologies are constructed for a better understanding of the aspects identified through semantic annotations. The results of the classification method are evaluated using the performance metrics accuracy, precision, recall, and F-measure for effective evaluation of the proposed method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Introduction to the tm Package, Text Mining in R, Ingo Feinerer, 6 Dec (2017)
An Article on “Big Data is the Future of Healthcare”, by cognizant 20-20 insights | Sep (2012)
Rani, V.V., Sandhya Rani, K.: Twitter Streaming and Analysis through R”. Indian J. Sci. Technol. Dec 2016
Fikri, M.: A comparative study of sentiment analysis using SVM and SentiWordNet. In: The 2nd International Conference on Informatics for Development (2018)
Go, A., Bhayani, R., Huang, L.: Twitter Sentiment Classification Using Distant Supervision. Stanford University, Stanford, CA (2017)
Kontopoulosa, E., Berberidisa, C., Dergiadesa, T., Bassiliadesb, N.: Ontology-based sentiment analysis of twitter posts. An Int. J., Expert Syst. Appl. Jan (2013)
Bindal, N., Chatterjee, N.: A two method for sentiment analysis of tweets. Int. Conf. Inf. Technol. IEEE (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Vasudha Rani, V., Sandhya Rani, K. (2020). Identification of Ontologies of Prediabetes Using SVM Sentiment Analysis. In: Venkata Krishna, P., Obaidat, M. (eds) Emerging Research in Data Engineering Systems and Computer Communications. Advances in Intelligent Systems and Computing, vol 1054. Springer, Singapore. https://doi.org/10.1007/978-981-15-0135-7_49
Download citation
DOI: https://doi.org/10.1007/978-981-15-0135-7_49
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0134-0
Online ISBN: 978-981-15-0135-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)