Publication Type:

Conference Proceedings


Advances in Intelligent Systems and Computing, Springer Verlag, Volume 415, p.1-13 (2015)





Artificial intelligence, Bagging, Boosting, Classification (of information), Ensemble learners, entropy, Feature extraction, Fuzzy inference, information dissemination, Learning systems, Maximum entropy methods, Sentiment classification, Social networking (online), Support vector machines, SVM


Twitter is considered to be the most powerful tool of information dissemination among the micro-blogging websites. Everyday large user generated contents are being posted in Twitter and determining the sentiment of these contents can be useful to individuals, business companies, government organisations etc. Many Machine Learning approaches are being investigated for years and there is no consensus as to which method is most suitable for any particular application. Recent research has revealed the potential of ensemble learners to provide improved accuracy in sentiment classification. In this work, we conducted a performance comparison of ensemble learners like Bagging and Boosting with the baseline methods like Support Vector Machines, Naive Bayes and Maximum Entropy classifiers. As against the traditional method of using Bag of Words for feature selection, we have incorporated statistical methods of feature selection like Point wise Mutual Information and Chi-square methods, which resulted in improved accuracy. We performed the evaluation using Twitter dataset and the empirical results revealed that ensemble methods provided more accurate results than baseline classifiers.


cited By 0; Conference of 5th International Conference on Fuzzy and Neuro Computing, FANCCO 2015 ; Conference Date: 17 December 2015 Through 19 December 2015; Conference Code:158209

Cite this Research Publication

L. K. Devi, Subathra P., Dr. (Col.) Kumar P. N., V., D. S. Ravi, and B.K., P., “Tweet Sentiment Classification Using an Ensemble of Machine Learning Supervised Classifiers Employing Statistical Feature Selection Methods”, Advances in Intelligent Systems and Computing, vol. 415. Springer Verlag, pp. 1-13, 2015.