Certain Analysis Of Authentic User Behavioral And Opinion Pattern Mining Using Classification Techniques

Abstract

With the fast growth of the Internet, the capability of users to generate and distribute content has formed active electronic communities that offer a wealth of product information. However, the high volume of reviews that are classically available for a single product makes harder for individuals as well as manufacturers to establish the best reviews and recognize the true underlying quality of a product. We re-examine the impact of reviews on economic outcomes like product sales and see how dissimilar factors involve social outcomes such as their apparent usefulness.

There are various methods have discussed for Estimating the Helpfulness and Economic Impact of Product Reviews by Mining Text and Reviewer Characteristics. Our approach explores multiple aspects of review text, such as subjectivity levels, various events of readability and amount of spelling errors to identify significant text-based features. In this paper we examine the economic impact of product reviews with maximum threshold and associated research work done in this area. Also comparisons are done between the variety of schemes to explain the advantages and limitations. In this paper, experimental evaluation shows that the performance analysis of the Estimating the Helpfulness and Economic Impact of Product Reviews on the basis of decision threshold, classification rate, false pattern rate and product index.