Computational performance is improved by use of basic feature selection in most of the research works. Sentiment analysis identifies whether opinion expressed on a topic in a document is positive or negative. But many potential sentiment analysis applications are not feasible because of the voluminous amount of features present in the corpus. This paper evaluates a range of feature selectors systematically with respect to their efficiency in improving the performance of the classifiers for sentiment analysis. Movie reviews are used for sentiment analysis in this study.