NOVEL DATAMINING TECHNIQUE FOR ONLINE SHOPPING

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1 NOVEL DATAMINING TECHNIQUE FOR ONLINE SHOPPING D. Ravi 1, A. Dharshini 2, Keerthaneshwari 3, M. Naveen Kumar 4 1,2,3,4 CSE Department, Kathir College of Engineering Abstract: Opinion mining is a type of NLP for tracking the mood of the public about a particular product. Here we have used computer products for the process. Admin clusters the user s opinion and their correlated (positive or negative) opinion of the products. The opinion will be predicted based on the users feedback. Advanced Products Review system that detects hidden sentiments in feedback of the customer and rates the products accordingly. Opinion Mining for Products Reviews is a web application which gives review of the feedback that is posted. The System takes feedback of various users, based on the opinion, system will specify whether the posted products are good, bad, or worst. We use a mysql DB of sentiment based keywords along with positivity or negativity weight in database and then based on these sentiment keywords mined in user feedback is ranked. Once the user login to the system he views the products and gives feedback about the restaurant. The role of the admin is to post new products and adds keywords in database. This application also works as an advertisement which makes many people aware about the products quality. When the user clicks on a particular restaurant, user can view the products and give comment about the restaurant. This system helps to find out good products. Keywords- fuzzy sets, knowledge-based recommendation, recommender systems, tree matching. I. EXISTING SYSTEM User has to read all possible reviews for selecting that product. Opinion mining techniques recognizes the polarity of each sentence in all reviews given to product, and then computes the total of all similar products using the standard Functions Existing system has provided one feature that anyone can give feedback about any product. This causes limitation for this framework. The person from challenging product website can give fake feedback to the original website. This can be done for achieving popularity in internet marketing. There is no special functionality describe in existing system for avoiding this fake reviewing. So existing system is able to mine negative and positive feedback but it fails to identify real one and fake one review in list of product review. If any product is having good quality but challenger user has enter inaccurate or negative review about product then other customers avoid to buy that product though the product is best. It will create a big loss for hosting website in terms of money, market position and customer feedback Disadvantages of existing system Feedback calculation is not accurate. Not convenient for user visualization. II. PROPOSED SYSTEM Here we have proposed a framework for avoiding acceptance of fake feedback. In this we are using predefined dataset for review. Then we are going to do extraction of opinion from that reviews. After that we will remove the common words which create effect of common objective. Then depending upon strength of remaining objective we are computing degree of adverbs. Next step is to check sentence polarity. The proposed system takes the dataset from the random online shopping website where the reviews about the two products.both the product reviews are worked on and according to polarity of the terms used in the reviews and comments, the products are ranked. DOI : /IJRTER NHZBR 1

2 Generally, the product having highest rank may not necessarily have good qualities, or might not be the best one, but after the use of product, the reviews that are given to the product are also the very important factor that matters a lot while giving reviews about the product 2.1. Advantages User can easily share his view about the products. People can easily decide whether the products are good or bad by using this application. III. MODULE DESCRIPTION 3.1. Administrator The administrator plays a major role in customizing web services and thereby helping users to gather information or purchase products according to their own needs. The admin can login to the website using his username and password. Once the admin has logged in, he can add, delete or update several products and information needed by the registered visitors of the website. In this project, the concept of Ontology is utilized by 4 web services. The web services include online book purchase, online shopping of mobile phones, online information, and education counseling information providers. Admin has the role to maintain information about all these services. He has the right to view database of registered customers and also to maintain the details about all the transactions made by each customer. The system implementation using clustering technique is as follows User Profile Creation: Users can create an account in the website by providing various details such as username, password, name, age, gender, date of birth, income status, caste category etc... Once after the successful account creation he/she can login to the website using his username and password. The personal details provided by the user are crucial for online shopping. When the user searches a product such as a book or mobile phone, the search results will be displayed. The users can also edit their profile information and update the database. A single user can view also the previous transactions made by him in the form of table. The details used for profile creation is privacy protected with a username and password Purchase/ Feedback The last module of this project is purchase/feedback. When the customers searching for a product are satisfied with the search result, they can make payment through online using credit card by clicking on the product. The click event will take them to the payment gateway. The customers can add description as feedback Risk Management: Risk Management is another important and challenging module in this project. In this module based on the user feedback, we can extract the positive and negative feedback words for estimating particular feedback are either positive or negative or intermediate category. We can see the all user feedback for all products. The accuracy of the prediction depends on the information a user provides at the stage of feedback The risk management is categories 3 ways in the project. They are, Extract positive or negative words for each product, based on user s feedback in our website and database. Extract positive or negative words for external Url Link. Extract positive or negative words for particular All Rights Reserved 2

3 4.1. ADMIN: IV. Admin SYSTEM ARCHITECTURE Username Login Add products Password Product name,details,quantity buyers list DATA BASE transaction details Profit details Retrieve from All Rights Reserved 3

4 4.2. USER: User From database User profile creation DB Login no.of.products Buy products 4.3. Product purchase: Customers From database Search products Payment Credit card Feedback for the All Rights Reserved 4

5 4.4. Opinion extraction: Data set or web URL Feedback extraction Find count of the opinion V. SYSTEM ENVIRONMENT 5.1. HARDWARE CONFIGURATION: System : HCL Processor : Pentium IV Processor Speed : 2.80GHz Main Storage : 512MB RAM Hard Disk Capacity : 80GB Floppy Disk Drive : 1.44MB CD-ROM Drive : LG 52X Reader Keyboard : 104 Keys Mouse : Logitech Monitor : Samsung 17 Color SOFTWARE CONFIGURATION Operating System : Windows XP Front end : Java Back End : All Rights Reserved 5