Detection of Fraud Reviews for a Product

IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 10, 2015 | ISSN (online): 2321-0613

Detection of Fraud Reviews for Products Dr. S.K. Pathan1 Janhavi Sankpal2 Pooja Sankpal3 Kanchan Haral4 1 Professor 2,3,4Student 1,2,3,4 Department of Computer Science 1,2,3,4 Smt. Kashibai Navale College of Engineering, Pune Abstract— Most of us use e-shopping (Any product) these days and refer its rating or reviews before we download or buy that product. Amazon/Play store provide a great number of products but unfortunately few of those product reviews are fraud. Hence such products must be marked, so that they will be recognizable for rest of the users. Here we are comparing reviews from two sites so that we can get more clear idea. We can get higher probability of getting real reviews if we take data from multiple sites. We are proposing a system to develop an android application that will take reviews from two different websites for single product, and analyze them with NLP for positive or negative rating. In this, user will give two different URLs of two different sites for same product to the system as input. For every URL reviews and comments will be fetched separately and analyzed with NLP for positive negative rating. Then their rating will be combined together with average to give final rating for the product. As we are handling the big data here, we are using Hadoops map reduce. So it will be easier to decide which product reviews are fraud or not.

Key words: Primal Text Mining and Pre-processing, NLP, Mapping Text Data I. INTRODUCTION This article is provided for the people who download or buy products online. Before they proceed with their purchase, review of that product is a key aspect. There is no means by which they can be sure that the given reviews are authentic.

A database is provided through which we can map the comments by which we can conclude which of the comments are positive and which are negative. User can check the reviews of the same product from two different sites. User will provide the URL of the one product from two different sites. After analysing the reviews and mapping with the database result will be generated. The generated result will be in the form of positive or negative and in terms of percentage and a graph. Using NLP we can process the reviews and generate results. To map the data with the database Hadoop’s MapReduce method can be used.

II. EXISTING WORK Many of the sites just provide ranking, rating and reviews.

They do not assure its authenticity. There is a need to cross check if the commented reviews are original or they are just for bumping up the product to gain popularity.

On this domain there are exist some related work, such as web ranking spam detection [1], online review spam detection [2] and mobile App recommendation systems [3], the issue in detecting ranking of the fraud for mobile applications or any online product is still unfathomed.

Currently, the most popular and widely used Online e-shopping sites provide facility for buyers to view review of the product they are interested to buy and for the

people already bought products to share their shopping experience and rate or review the product. This helps future buyers to have the base of reference to which they can refer before buying products online. Even on platforms like Play store, one gets an opportunity to study reviews before actually downloading the application. However, as the positive side enables us to these facilities of studying the experiences of users on the other hand there also raises questions about authenticity of these reviews. In this digital world it has become easier to rate any products online.

If we consider real-world observations, we find that each review is always associated with a respective untapped topic. For instance, some reviews might be related to the untapped topic “worth a try” while some might be related to the untapped topic “not so good”. At the same time, different buyers have different personal preferences of mobile applications or different site preferences for buying products. A product or an application may have different topic distributions in their historical review records.

Plausibly, the topic distribution of reviews, be it of a product or an application, in a normal leading session of application, should be consistent with the topic distribution in all historical review records of that application. And so applies to the product reviews. The possible cause to this are the reviews of the topics that are based on the user’s personal usage experiences and choices but not the popularity of mobile applications. On the other hand, if the reviews of leading sessions have been manipulated, the two topic distributions will be prominently different. For example, there may contain more positive topics, such as “worth a try”, “popular” and “good” in the leading session.

III. DRAWBACKS OF THE EXISTING SYSTEM The existing system cannot determine the authenticity of the reviews which are posted for any product.

Buyers or the users are not able to recognize the authenticity of the reviews given to a product and hence they face dilemma whether the product is really good or bad.

The existing system does not detect fraudulent or deceptive activities in reviews which might be just for gaining popularity of that product.

For example, the developer or the brand which are selling their products can themselves rate the products to manipulate consumer’s perception. Also the rivals or the opponents can post negative reviews for inflating the product’s reputation.

Table 1 After using these search words both, positive keywords classification and negative keywords classification are used which leads to the generation of result from the keywords in the database [10].

All rights reserved by www.ijsrd.com

408

Detection of Fraud Reviews for Products (IJSRD/Vol. 3/Issue 10/2015/082)

A. Primal Text Mining and Pre-processing: Many software’s / algorithms like Rapid-miner, WorldNet, and data collection and processing trees have been developed to test common positive and negative words.

comment or reviews about it. 2) Use NLP for processing those comments and reviews. 3) Use Map reduce Algorithm for querying. Hence we can detect whether the reviews fraud or not.

The system uses the following basic concepts: A. Natural Language Processing: It is a field of computer science, artificial intelligence, and computational linguistics concern with interactions between computers and human languages.

Fig. 1: A sample architecture [10] The URLs from two different sites for the same product are taken as input in the system. The reviews from those sites are scanned and mapped into the database to get the result in terms of positive and negative percentage. A database is maintained to map the words which are used in the comments to check the positivity or the negativity of the comments or reviews. Based on these results, a user can roughly get an idea of whether the product (application or any product) is worth buying or downloading.

B. Mapping Text Data: The final word set will be pruned using the above mentioned methods, with the results displayed in Table 1,2 which have separate column of both positive and negative words.

Positive Negative

B. Tokenization: It transforms a stream of characters into a stream of processing units called tokens.

C. Stop Words Filtering: It consists in eliminating stop words. Stop words or words are filtered out before or after natural language processing.

For example, “is”,”the”,”at”,”who”,”which”.

D. Stemming: It is the process of reducing derived words into their root form. The related words should match to the same stem.

For example, the words “drove”, “drives”, “driven” would fall under there root word “drive”.

V. CONCLUSION Thus, if the gathered data is processed and studied precisely then it will lead to better judgment of the products, the developers will no longer bring their applications to market just for the sake of money, people will get idea about its quality and reliability easily. Reviews will easily be detected and would not be taken into consideration unless they are truly genuine.

Agree

Bad

Appreciate

Cannot

Beneficial

Damage

Comfort

Dangerous

Ease

Depression

ACKNOWLEDGEMENTS

Easier

Died

Enjoy

Difficult

Good

Error

Great

Hard

Greatest

Failure

Help

Impossible

Hope

Lack

Important

No

We take this chance to convey our sincere gratitude, respect and deep regards to our Project guide, Dr.S.K.Pathan, for his encouraging guidance, monitoring and constant motivation throughout the process of writing this research paper.

The blessing, help and guidance given by him shall from time to time, carry us a long way on the journey of life which we are about to embark.

We are also competed to Prof.Pramod Mali, our Project Coordinator, for the valuable information and guidance provided by him.

Thanks

Not

REFERENCES

Superb

Poor

Yes

Weak

[1] Online: https://en.wikipedia.org/wiki/Information_retrieval [2] E.-P. Lim, V.-A. Nguyen, N. Jindal, B. Liu, and H. W. Lauw, “Detecting product review spammers using rating behaviours,” in Proc. 19th ACM Int. Conf.

Inform. Knowl. Manage., 2010, pp. 939–948 [3] A. Ntoulas, M. Najork, M. Manasse, and D. Fetterly, “Detecting spam web pages through content analysis,” in Proc. 15th Int. Conf. World Wide Web, 2006, pp.

83–92.

[4] N. Spirin and J. Han, “Survey on web spam detection: Principles and algorithms,” SIGKDD Explor.

Newslett., vol. 13, no. 2, pp. 50– 64, May 2012.

Table 2: Positive and Negative Classification of Words from Posts IV. PROPOSED SYSTEM Our main aim is to provide users with the authenticity of product reviews and authenticity of application reviews.

This will enable user to have a clearer view towards the quality and reliability of the product or application.

We therefore propose a system which will perform the following tasks: - 1) System that will collect all the information about particular application/sites such as any

All rights reserved by www.ijsrd.com

409

Detection of Fraud Reviews for Products (IJSRD/Vol. 3/Issue 10/2015/082)

[5] G. Heinrich, Parameter estimation for text analysis, “Univ. Leipzig, Leipzig, Germany, Tech.Rep., http://faculty.cs.byu.edu/~ringger/CS601R/papers/Hein rich-GibbsLDA.pdf, 2008.

[6] N. Spirin and J. Han, “Survey on web spam detection: Principles and algorithms,” SIGKDD Explor.

Newslett., vol. 13, no. 2, pp. 50– 64, May 2012.

[7] B. Zhou, J. Pei, and Z. Tang, “A spamicity approach to web spam detection,” in Proc. SIAM Int. Conf. Data Mining, 2008, pp. 277–288.

[8] Z. Wu, J. Wu, J. Cao, and D. Tao, “HySAD: A semisupervised hybrid shilling attack detector for trustworthy product recommendation,” in Proc. 18th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2012, pp. 985–993.

[9] S. Xie, G. Wang, S. Lin, and P. S. Yu, “Review spam detection via temporal pattern discovery,” in Proc. 18th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2012, pp. 823–831.

[10] Altug Akay, Andrei Dragomir and Bj orn Erik Erlandsson, Senior Member, IEEE. “Network Based Modeling and Intelligent Data Mining of Social Media for Improving Care”, IEEE Journal of Biomedical and Health Informatics, Vol. 19, No. 1, January 2015.

[11] H. Zhu, E. Chen, K. Yu, H. Cao, H. Xiong, and J. Tian, “Mining personal context-aware preferences for mobile users,” in Proc. IEEE 12th Int. Conf. Data Mining, 2012, pp. 1212–1217

All rights reserved by www.ijsrd.com

410