Feature selection, also known as attribute selection or variable subset selection, is the process of selecting a subset of relevant features. It has been the focus of interest for quite some time and much work has been done. With the creation of huge databases and the consequent requirements for good machine learning techniques, new problems arise and novel approaches to feature selection are in demand. In previous research recognized this important issue and propose, wordnet and pos tagger tools are used to minimize the redundancy between sequentially selected features by calculating efficiency and opinion strength of features. Sentiment analysis (also known as opinion mining) refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. Sentiment analysis is widely applied to reviews and social media for a variety of applications, ranging from marketing to customer service. Our new model used to identify the polarity of features, need to extract the sentiment features from text. We are using senti wordnet tool to performing the sentiment analysis. To separate the sentiment features, we are using part of speech tagger.