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Last few years have delivered a huge boom in the field of research in Sentiment Analysis, totally on incredibly subjective textual content kinds like films or products evaluations. This research work design a feature selection technique with Bagged Random Forest classification to predict the sentiments of news articles. This work presents comparison among C4.5, random forest algorithm, bagged random forest algorithm and proposed algorithm on the basis of various parameters. Results are evaluated on the basis of various parameters such as correctly classified instances, incorrectly classified instances, error comparison on basis of mean absolute error, root mean squared error, relative absolute error, root relative squared error, average true positive rate, average false positive rate, recall and F-measure. These new results have proved that the proposed technique is having better results than the previous one.