An ensemble approach to stabilize the features for multi-domain sentiment analysis using supervised machine learning

Abstract

Sentiment classification or sentiment analysis has been acknowledged as an open research domain. In recent years, an enormous research work is being performed in these fields by applying numerous methodologies. Feature generation and selection are consequent for text mining as the high dimensional feature set can affect the performance of sentiment analysis. This paper investigates the inability of the widely used feature selection method (IG, Chi Square, Gini Index) individually as well as their combined approach on four machine learning classification algorithm. The proposed methods are evaluated on three standard datasets viz. IMDb movie review, electronics and kitchen product review dataset. Initially, select the feature subsets from three different feature selection methods. Thereafter, statistical method UNION, INTERSECTION and revised UNION method are applied to merge these different feature subsets to obtain all top ranked including common selected features. Finally, train the classifier SMO, MNB, RF, and LR (logistic regression) with this feature vector for classification of the review data set. The performance of the algorithm is measured by evaluation methods such as precision, recall, F-measure and ROC curve. Experimental results show that the combined method achieved best accuracy of 92.31 with classifier SMO, which is encouraging and comparable to the related research.