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Abstract:

                 In
today’s life e-commerce is growing and becoming popular day-by-day. the people
require review about a product before spending their money on the product. the
people  can check if the product
reviews  are genuine  or 
fake. sometimes  the firms
are  give 
the good reviews for many different products manufactured by their own
firm . so the  people will not be able to
find out whether the review is genuine or fake . so the  “Fake Product Review Monitoring and
Removal for Genuine Online Product Reviews Using Opinion Mining”  is introduced.  This is used to find out  the 
fake reviews made by posting fake comments about a product .It will
be  identifying by IP address and the
user will give the opinion for the particular 
product with the help of one time password. if there is any  fake review are  viewed the admin will removed the particular
fake review. This system uses data mining methodology. This system helps the
user to find out correct review of the product.

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keyword: fake product, fake review,
opinion mining

I.
Introduction:

            Data mining is the process of
analyzing data from different perspectives and summarizing it into useful
information – information that can be used to increase revenue, cuts costs, or
both.

             The e-commerce has facilitated consumers to
purchase products online. The review have become an important resource for both
consumers and firms.  Consumer’s commonly
seek quality information from online consumer reviews prior to purchasing a
product, while many firms use online consumer reviews as an important resource
in their product development, marketing, and consumer relationship management.

For
example a smart phone for which one user rates a 2 star rating, and another
user give a 5 star rating. In this case, it is difficult to conduct a quality
evaluation fairly.

            Generally, identifying
the important product aspects will benefit both consumers and firms. Consumers
can conveniently make wise purchase decision by paying attentions on the
important aspects, while firms can focus on improving the quality of these
aspects and thus enhance the product reputation effectively. However, it is
impractical for people to identify the important aspects from the numerous
reviews manually. Thus, it becomes a compelling need to automatically identify
the important aspects from consumer reviews.

ii  Related Work

            This
paper we flew with some works for handling opinion mining patterns which was
voted by the customers on the web and gathered the details about smart phones rating
and brand names so that it will be helpful for the opinion mining. The
necessary details about smart phones .    

1)
Gathered the smart phone details

2) Opinion mining or sentiment
analysis

 

The smart phone details
for rating:

Customer id

Brand

Rates(5)

167v01

APPLE

3

801×25

Moto

4

279t45

Samsung

3

345h76

Lenovo

4

167v23

Apple

4

345h56

Lenovo

2

801×35

Moto

3

167v55

APPLE

3

801×55

Moto

4

279t145

Samsung

3

345h66

Lenovo

4

167v105

Apple

4

345h176

Lenovo

2

801×125

Moto

3

 

Opinion
mining or sentiment analysis:

          Opinion mining, which is also called
sentiment analysis, involves building a system to collect and categorize
opinions about a product.  we first identify the aspects in the reviews
and then analyze consumer opinions on the aspects via a sentiment classifier.

 

III.
EXISTING SYSTEM:

 

        The users will spend their quality time
into reading other user reviews if they are available. survey performed by
Yelp.com has shown that:

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

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

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

 

 IV. PROPOSED SYSTEM:

 

           The proposed System supports, a
product aspect ranking framework to automatically identify the important
aspects of products from numerous consumer reviews.

 

 

 

        

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The above
diagram shows the system representation of 
the  proposed system. Now we will
see flow of the system systematically.

1) First
enters the customer id and name.

2) After
entering the name of the phone.

3) Fetch the
reviews given by the different reviewers or customers.

4) After
that clustering algorithm is implemented for clustering the reviews in the
groups.

5) After completing
the process of clustering, the ARFF file is generated.

6) This ARFF
file given as a input to the classifier, we used J48 classifier for the
detecting the reviews. Training and testing process are done by the J48 classifier.

7) After
completing the process of classification, fake and truthful reviews are
detected. These reviews now qualify for the further checking for Brand Spam detection.

 

V. Data mining
Methodology

Clustering:(Un Supervised)

1. A cluster is a subset of
objects which are “similar”

2. A subset of objects such that
the distance between any two objects in the cluster is less than the distance
between any object in the cluster and any object not located inside it.

3. A connected region of a
multidimensional space containing a relatively high density of objects.

VI. EXPERIMENTAL STATUS:

 

            The experimental setup requires for
the proposed system is represented in tabular format.

Customer id

Brand Name

Rates(5)

167v01

APPLE

3

801×25

Moto

4

279t45

Samsung

3

345h76

Lenovo

4

167v23

Apple

4

345h56

Lenovo

2

801×35

Moto

3

167v55

APPLE

3

801×55

Moto

4

279t145

Samsung

3

345h66

Lenovo

4

167v105

Apple

4

345h176

Lenovo

2

801×125

Moto

3

 

    SMARTPHONE
BRANDS:

 SMART
PHONE RATE:

 

    VII CONCLUSION:

 

                In this
paper the  framework contains  three main components, i.e., product aspect
identification, aspect sentiment classification, and aspect ranking. The
feedback and viewpoints for decision making is uses by Web users and companies.
Exploring ways to learn  behavior  patterns related to that spamming so as to
improve the accuracy of the current regression model is also  an interesting research direction.

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