online customer reviews is a great platform for collecting large volume of information for sentiment analysis. Users of the online shopping site Amazon are confident to post reviews of the item that they purchase. A Little attempt is made by Amazon to restrict or limit the content of these reviews. We utilize product clients review comments about product and review around retailers from Amazon as data set and classify review content by subjectivity/objectivity and negative/positive state of mind of buyer. Such reviews are helpful to some extent, promising both the shoppers and products makers. This paper presents an experimental study of efficacy of classifying item review by posting the keyword. The Classification algorithm uses only the overall review scores to understand sentiment behind each review and extract the important aspects about the product. We developed an efficient classifier form to categorize the provided review is either a positive review or negative review by analyzing the presentation of different classification algorithm on the review data corpus. Clustering techniques are used to identify key sentiment characteristics to provide them to the users, which helps the user to understand the aspects of the products/service they wish to buy or experience.

opining mining or sentiment analysis, natural language processing, Amazon reviews, learning automata, machine learning

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