Ensemble Based Classification Methods for Twitter Sentiment Analysis

Title: Ensemble Based Classification Methods for Twitter Sentiment Analysis
Publisher: Guru Nanak Publications
ISSN: 2278-0947
Series: Volume 5 Issue 2
Authors: M.Govindarajan


Twitter has become a popular social media service often referred to as a micro- blogging site. The informal nature of Twitter leads to a lot of sentiments being posted and this has made twitter a gold mine for sentiment analysis. In this research work, new ensemble classification methods are proposed for homogeneous ensemble classifiers using bagging and heterogeneous ensemble classifiers using arcing classifier and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Naive Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by means of twitter dataset that is widely used in the field of sentiment classification. The accuracy of base classifiers is compared with homogeneous and heterogeneous models for Twitter dataset. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and also heterogeneous models exhibit better results than homogeneous models for twitter dataset.


Classification, Data Mining, SVM, Data Set.

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