Social networking sites play an important role in our day to day life. Data generated from these sites is large in amount. Here sentiment analysis is used to analyze such large amount of data and classify the text into different polarity. Sentiment analysis helps business and organization because it’s easy for them to know how people feel about their product or services so that they can make a better decision or improve their services. Data is collected from twitter. Existing sentiment analysis was established on the multinomial naïve bays where TF-IDF is used as feature extraction. In this paper, multinomial naïve Bayes is used as classifier and TF and Count Vectorizer hybrid approach is used at the time of feature extraction and used random forest classifier as feature selection. It also focuses on parameters like precision, recall, and f1-measure.

sentiment analysis, removing re-tweet, hybrid approach (TF, Count Vectorizer), Random forest as feature selection.

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