Sri Venkateswara College of Engineering (SVCE), Tirupati Aspect Ranking Based On Products and its Applications by Using Web Application Presented by K.MANOJ KUMAR Assistant Professor Department of CSE Sri Venkateswara College of Engineering (SVCE), Tirupati ICONG2E2C2-2016-GEC1123

Abstract Numerous client reviews of products are increasing rapidly on the web. Client reviews contain wealthy and valuable knowledge for both firms and users. The vital aspects are typically commented on by an outsized range of consumers. Client opinions on the vital aspects greatly influence their overall opinions on the merchandise. This paper demonstrates the aspect identification, eliminating the fake reviews of unknown users and aspect ranking. ICONG2E2C2-2016-GEC1123

Introduction Most retail websites encourage consumers to put in writing reviews to specific their opinions on numerous aspects of the product. Billions of product reviews are offered by numerous users in recent years . Lot of genuine reviews are given by some unknown users, market competitors etc. By identifying the product aspects and aspect ranking, it is helpful for both users and firms. ICONG2E2C2-2016-GEC1123

Existing System Unauthenticated users are mostly writing reviews on different products without purchasing of that particular product in e- commerce websites. By this genuine review & rating of an individual product is degraded. Identifying the product vital aspects is a crucial task. Customers are going for incorrect products by this type of unauthenticated reviews. ICONG2E2C2-2016-GEC1123

Proposed System In proposed system, consumers will get right to give review after authentication of product number, which he will get after purchase of an product. Product Number is checked in the database for authentication. After the review submission, the entire review is analyzed for identifying the aspects in that particular review by sentiment analysis. Overall product rating and each aspect ranking will be retrieved after analyzing the all reviews of an particular product by support vector machine. Like and Dislike option is also provided for each aspect in an particular product. ICONG2E2C2-2016-GEC1123

Advantages Customers will purchase genuine products by this type of verified reviews. Aspect Ranking will be helpful for the both users and merchants for in depth analysis of each product. Quality of the product is evaluated properly by aspect ranking. Each and every aspect of an product can be identified and analyzed efficiently. ICONG2E2C2-2016-GEC1123

Architecture Diagram ICONG2E2C2-2016-GEC1123

Implementation Flow Diagram ICONG2E2C2-2016-GEC1123

Algorithms Used Support Vector Machine(SVM) Naive Bayes Classifier Sentiment Classification on Product Aspects ICONG2E2C2-2016-GEC1123

Conclusion By this Paper, fake reviews can be eliminated effectively by this authentication method of an particular user. Product Quality will be justified genuinely. Aspect Ranking is helpful to improve the users view of thinking and merchants can get genuine feedback of his products. Each aspect of an product will be rated using like and dislike option. ICONG2E2C2-2016-GEC1123

References [1] B. Liu, Sentiment Analysis and Opinion Mining. Mogarn & Claypool Publishers, San Rafael, CA, USA, 2012. [2] L. M. Manevitz and M. Yousef, “One-class SVMs for document classification,” J. Mach. Learn., vol. 2, pp. 139–154, Dec. 2011. [3] A. Ghose and P. G. Ipeirotis,“Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics,” IEEE Trans. Knowl. Data Eng., vol. 23, no. 10, pp. 1498– 1512. Sept. 2010. [4] ComScore Reports [Online]. Available: http://www.comscore.com/Press_events/Press_releases, 2011. ICONG2E2C2-2016-GEC1123

References(Contd.) [5] F. Li et al., “Structure-aware review mining and summarization,” in Proc. 23rd Int. Conf. COLING, Beijing, China, 2010, pp. 653–661. [6] J. R. Jensen, “Thematic information extraction: Image classification,” in Introductory Digit. Image Process. pp. 236–238. [7] V. Gupta and G. S. Lehal, “A survey of text summarization extractive techniques,” J. Emerg. Technol. Web Intell., vol. 2, no. 3, pp. 258–268, 2010. ICONG2E2C2-2016-GEC1123

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