Abstract
Sentiment analysis is an active research area which deals with information extraction and knowledge discovery from text using Natural Language Processing and Data Mining techniques. Sentiment analysis, also known as opinion mining, plays a major role in detection of customer’s attitude, response and opinion towards a product or service. The aim of this paper is to perform sentiment analysis on a particular service to discover how users perceive the service automatically. Data is extracted from twitter, pre-processed and classified according to the sentiment expressed in them: positive, negative or neutral using five supervised learning classifiers-The Naïve Bayes, Multinomial Naïve Bayes (MNB), Bernoulli Naïve Bayes (BNB), Linear Support Vector Machine (SVM) and Decision Tree classifiers. Finally, the performance of all the classifiers is compared with respect to their accuracy. In addition, the results from the classifiers show that supervised learning classifiers perform excellently in sentiment classification.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dodd, J.: Twitter sentiment analysis (2014). http://trap.ncirl.ie/1868/1/johndodd.pdf. Accessed 18 Mar 2017
Garg, P.: Sentiment analysis of Twitter data using NLTK in Python, June 2016. http://dspace.thapar.edu:8080/jspui/bitstream/10266/4273/4/4273.pdf. Accessed 15 Feb 2017
Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of LREC (2010)
Read, J.: Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: Proceedings of the ACL Student Research Workshop, pp. 43–48. Association for Computational Linguistics (2005)
Parikh, R., Movassate, M.: Sentiment analysis of user-generated Twitter updates using various classification techniques (2009)
Kharde, V., Sonawane, S.: Sentiment analysis of Twitter data: a survey of techniques. Int. J. Comput. Appl. 139(11), 5–15 (2016)
Suresh, A., Bharathi, C.R.: Sentiment classification using decision tree based feature selection. IJCTA 9(36), 419–425 (2016)
Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417–424. Association for Computational Linguistics (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Muyili, A.O., Sennaike, O.A. (2018). Comparative Analysis of Supervised Learning for Sentiment Classification. In: Odumuyiwa, V., Adegboyega, O., Uwadia, C. (eds) e-Infrastructure and e-Services for Developing Countries. AFRICOMM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 250. Springer, Cham. https://doi.org/10.1007/978-3-319-98827-6_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-98827-6_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98826-9
Online ISBN: 978-3-319-98827-6
eBook Packages: Computer ScienceComputer Science (R0)