DETECTING FRAUD USING SENTIMENT ANALYSIS

ABSTRACT

Merchants selling products on the Web often ask their customers to review the products that they have purchased and the associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions. The Detection of Reviews using Sentiment analysis deals with finding a positive review from thousands of reviews. As the numbers of customers are growing, reviews received by products are also growing in large amount. Thus, mining opinions from product reviews is an important research topic. In the past decade considerable research has been done in academia. However, existing research is more focused towards categorization and summary of such online opinions.

CHAPTER ONE

1.0 INTRODUCTION:

This chapter gives an overview on the aims, objectives, background of the study, and the scope of study, methodology, and significance of study and project outline.

1.1 BACKGROUND OF THE STUDY

Nowadays, there are so many applications available on internet because of that user cannot always get correct or true reviews about the product on internet.

In this project, we propose the system by developing web application which helps to detect fraud apps using sentiment comments and data mining.

We can check for user’s sentimental comments on multiple applications. The reviews may be fake or genuine. But after comparing reviews of admin as well as user’s, we can get more clear idea.

Hence, we can get higher probability of getting real reviews. So we are proposing a system to develop a web application that will take reviews from registered users for single product, and analyse them for positive negative rating.

For every user reviews and comments will be fetched separately and analysed for positive negative rating. Then their rating/comments will be judged by the admin and it would be easy for admin to predict the application as Genuine or Fraud.

In Review Based Evidences, besides ratings, most of the App stores also allow users to write some textual comments as App reviews. Such reviews can reflect the personal perceptions and usage experiences of existing users for particular mobile Apps. Indeed, review manipulation is one of the mast important perspectives of App ranking fraud.

1.2 STATEMENT OF THE PROBLEMS

There are problems found in online fraud both on mobile and web which include:

·         Advertising fake products for sales.

·         Frustrating customers until all the money they needed has been gotten from them.

·         Duping customers through phone calls, emails and text messages.

1.3 OBJECTIVE OF THE STUDY

The mobile industry is developing rapidly; therefore the numbers of mobile applications are increasing day by day in the market. As there are many apps available in market users are in fuzzy state while downloading the apps for their use. Different App stores like Google play store and Apple store launched their leader board on daily basis to inspire the users to download most popular applications by observing the ranking of applications. In fact to advertise a particular mobile Apps, leader board of apps is the most important way in the market. An app which is at the top on the leader board leads to large number of downloads and it will gain maximum profit. In order to have their Apps ranked as high as possible, app developers promote their apps using various ways such as advertising, offers etc. Such applications damage to phone and also may cause data thefts. Hence such applications must be identified, so that they will be identifiable for play store users. So we are proposing an android application which will process the information, comments and three reviews of the application with natural language processing to give results. So it will be easier to decide fraud application. The main objectives are,

1.  To rank fraud for mobile application.

2.  To improve the fraud detection efficiency.