DETECTING FRAUD USING SENTIMENT ANALYSIS
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DETECTING FRAUD USING SENTIMENT ANALYSIS
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.
DETECTING FRAUD USING SENTIMENT ANALYSIS
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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... computer science project topics
DETECTING FRAUD USING SENTIMENT ANALYSIS