FAKE REVIEW DETECTION

ABSTRACT:-

          Seller selling products on the web often ask or take reviews from customers about the products that they have purchased. As e-commerce is growing and becoming popular day-by-day, the number of reviews received from customer about the product grows rapidly. For a popular product, the reviews can go upto thousands. This creates difficulty for the potential customer to read them and to make a decision whether to buy or not the product. Problems also arise for the manufacturer of the product to keep track and to manage customer opinions. And also additional difficulties are faced by the manufacturer because many other merchants sites may sell the same product at good ratings and the manufacturer normally produces many kinds of products. In this research, we aim to summarize all the customer reviews of a product and compare the products based on reviews can be done on one place. This summarization task is different from traditional text summarization, because we only mine the information of that product on which the customers have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a rewrite some of the original comment, from the reviews to capture the main points as in the classic text summarization. Our task is performed in steps: (1) while login the customer will be verified using his/her e-mail id; (2) mining product features that have been commented on by customers; (3) identifying opinion sentences in each review and deciding whether each comment positive or negative; (4)and while giving opinions if its fake then e-mail id is blocked; (5) summarizing the results. This paper proposes several novel techniques to perform these tasks and Our experimental results using reviews of a number of products sold online demonstrate the effectiveness of the techniques.

ADVANTAGES OF THE PROJECT:-

• Helps to reduce fake reviews
• User can buy genuine product by viewing reviews
• Can help in many fields

EXISTING SYSTEM:-

 When performing any type of internet shopping, many of the users will spend their quality time into reading other user reviews if they are available. A survey performed by Yelp.com has shown that:

 More than 80% of users and shoppers do check and rely on reviews of the people.

 50% rely on ratings of the online product they want to buy.

 30% of the users compare the product’s reviews with others product’s reviews to get a reliable and trustworthy thing.

Clearly consumers value the feedback given by other users as do the companies that sell such products. Blogs, websites, discussion boards etc. are a repository of customer suggestions which are valuable and important sources of textual data. Therefore, today’s individuals and older ones extensively rely on reviews available on line. It means that people make their decisions of whether to purchase the products or not by analyzing and reflecting the existing opinions on those products. The fact that is if the potential customer or users gets a genuine overall impression of a product by

considering the present affect for that product, it is highly probable that he will actually purchase the product. Normally if the percentage of positive and effective opinions is considerable, it is likely that the overall impression will be highly positive. Likewise, if the overall impression is not proper, it is doubtful that they don’t buy the product. Now the customers can write any opinion text, this can motivates the individuals, and organizations to give undeserving spam opinions to promote or not to credit some target products, services, organizations, individuals, and even ideas without disclosing their true intentions. These spammed opinion information is called opinion spam.

PROPOSED SYSTEM:-

As most of the people require review about a product before spending their money on the product. So people come across various reviews in the website but these reviews are genuine or fake is not identified by the user. In some review websites some good reviews are added by the product company people itself in order to make product famous this people belong to Social Media Optimization team. They give good reviews for many different products manufactured by their own firm. User will not be able to find out whether the review is genuine or fake. To find out fake review in the website this “Fake Product Review Monitoring and Removal for Genuine Online Product Reviews Using Opinion Mining” system is introduced. This system will find out fake reviews made by the social media optimization team by identifying the IP address. User will login to the system using his user id and password and will view various products and will give review about the product. And the user will get genuine reviews about product. And while reviewing he needs to enter the email id from which he is reviewing and it would be verified. If he writes a fake review then his id will be blocked bot allowing him to share his opinions again.

MODULES:

• User Register: User have to register to check whether product review is Real or Fake.
• User Login: User have to register to check whether product review is Real or Fake.
• Processing Sentence: Use Natural language processing (NLP) to makes possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important.
• Word to Vector: Tf-idf is used to transforming text into a numerical feature is called text vectorization which mathematically eliminates naturally occurring words in the English language, and selects words that are more descriptive of your text.
• Classifying Sentence: Based on Tf-idf vectors machine learning algorithms (SVM, Naive bayes, Random Forest, Decision tree) will classify text.
• Buy/add to cart product: User can add any product into cart and can make dummy payment.

HARDWARE AND SOFTWARE REQUIREMENTS

HARDWARE:

• Processor: Intel Core i3 or more.
• RAM: 4GB or more.
• Hard disk: 250 GB or more.

SOFTWARE:-

• Windows Operating System.
• Java
• R (3.4.1)
• R Studio