Performance Assessment of Multiple Classifiers Based on Ensemble Feature Selection Scheme for Sentiment Analysis

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 various numbers of methodologies. Feature generation and selectionare consequent for text mining as the high-dimensional feature set can affect the performance of sentiment analysis. This paperinvestigates the inability or incompetency of the widely used feature selection methods (IG, Chi-square, and Gini Index) withunigram and bigram feature set on four machine learning classification algorithms (MNB, SVM, KNN, and ME). The proposedmethods are evaluated on the basis of three standard datasets, namely, IMDb movie review and electronics and kitchen productreview dataset.Initially, unigram and bigram features are extracted by applying n-gram method. In addition, we generate a compositefeatures vector CompUniBi (unigram + bigram), which is sent to the feature selection methods Information Gain (IG), Gini Index (GI),and Chi-square (CHI) to get an optimal feature subset by assigning a score to each of the features. These methods offer a ranking tothe features depending on their score; thus a prominent feature vector (CompIG, Comp GI, and CompCHI) can be generated easilyfor classification. Finally, the machine learning classifiers SVM, MNB, KNN, and ME used prominent feature vector for classifying the review document into either positive or negative.The performance of the algorithm is measured by evaluation methods such as precision, recall, and F-measure. Experimental results show that the composite feature vector achieved a better performance than unigram feature, which is encouraging as well as comparable to the related research. The best results were obtained from the combination of Information Gain with SVM in terms of highest accuracy