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Twitter is a medium that we can use for communication. All posted tweets we can store in one location and create archive. Archive contains new and old tweets. Now we can start the analyzation on archive tweets that's we can design effective sentiment analysis system. This paper main aim is to determine parts of speech opinion words using polarity classification technique and support vector machine learning algorithm. Surveys of methods are used in various levels of sentiment analysis. It does analyze the tweets information in limited levels of content only. Now in this paper we design new sentiment analysis tool using polarity classification technique. Polarity classification techniques discover top 20-emoticons, learning different classes of words and other features information. These techniques perform in depth tweets analysis. It does provide better analysis results compare to previous methods.

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