IMPROVED FEATURE SELECTION FORCLASSIFICATION OF SENTIMENTAL REVIEWS USING N-GRAM MACHINE LEARNING APPROACH

The sentiment analysis approach is used to determine the sentiment in the text content by using the keyword intensity or term frequency based approach. The keyword extraction models are used to determine the words containing the sentiment from the text data, and eliminate the remaining content based upon the selection or design of the feature extraction model. The keywords based features are then transformed to the numeric formation by using the ratio, weight or appearance based description, and further classified using the supervised classification model to identify its orientation. In this paper, the supervised machine learning approach combines the count vectorization and TF-IDF based features with Chi-square based feature selection for sentiment analysis in the IMDB review database. The proposed feature description model combines the various N-gram features, such as unigram, bigram and trigram, which signify the different aspects of sentiment contained in the text data. The proposed model has outperformed the existing model based upon the layered model using a count based method with TF-IDF. Support Vector Machine (SVM) classification method is considered as the best method after the result evaluation with the proposed feature descriptor.