SPAM REVIEW DETECTION ON E-COMMERCE SITES

Transcription

1 International Journal of Civil Engineering and Technology (IJCIET) Volume 9, Issue 7, July 2018, pp , Article ID: IJCIET_09_07_123 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed SPAM REVIEW DETECTION ON E-COMMERCE SITES Nandhini.G MS (Software Engineering), School of Information Technology and Engineering, VIT University, Tamil Nadu, India T. Muthamilselvan School of Information Technology and Engineering, VIT University, Tamil Nadu, India I. Alagiri School of Information Technology and Engineering, VIT University, Tamil Nadu, India P. Thanapal School of Information Technology and Engineering, VIT University, Tamil Nadu, India ABSTRACT Buying and selling the goods and services through internet called as electronic network known to be E-commerce. Due to the convenience of e-commerce, the number of users are increased. Meanwhile, the people review of product also increased. In e- commerce websites, fake review is often the major problem. Nowadays, it is known to be common that user can write the review for their purchased product. There are many ways that user can write reviews. Using this opportunity, there is a possibility that spammers can leave fake review. Many users determine the quality of product based on user s reviews. So, the fake review creates lot of problems on product quality, sales, and economic growth. To tackle this problem, we are going to use Naive Bayesian classifier which is very simple and easy technique to classify the product review. Feature extraction can be used to extract the feature.here we are using dataset for classifying the product reviews. Here our aim is to find fake reviews. By detecting fake reviews the accuracy of e-commerce system can be improved. Index Terms: E-Commerce, Product Review, Naive Bayesian Classifier, Spammers. Cite this Article: Nandhini.G, T. Muthamilselvan, I. Alagiri and P. Thanapal, Spam Review Detection on E-Commerce Sites, International Journal of Civil Engineering and Technology, 9(7), 2018, pp editor@iaeme.com

2 Nandhini.G, T. Muthamilselvan, I. Alagiri and P. Thanapal 1. INTRODUCTION The way the people express their opinions and communicate with others on the web is radically changed. Nowadays, customers rely a lot on the written reviews before going to purchase the product. There are basically two types of spam reviews. First type is positive/negative reviews and second type is no reviews [7]. The positive/negative reviews of product gives opinion on their product selection. So, positive review makes good opinion about the product as well as increases the quality of product while negative review makes bad impression about product as well as destroys the reputation of product.second type, no reviews (e.g. Ads) has no opinion on the product. It is also a kind of encouragement/discouragement for customer to buy the product. The growth of e-commerce sites also increase the number of spam reviews. As results shown [5], 20% of reviews on Yelp website were actually fake. In order to detect the fake reviews we are going to utilize feature extraction technique and classification method. There are several algorithms for classifying the spam and non-spam data such as vector machine, decision tree, Naïve Bayes, neural network are well-known classifiers [2]. For process and analyze the data set, we first apply feature extraction technique over the selected attributes. Overall knowledge of various classifier, we find that the accuracy can be improved using Naïve based classification technique. The performance of Naïve based classifier was found to be better than other classifiers. 2. EXISTING SYSTEM Spam review is predominant in e-commerce websites. There are many complications created by the spam reviews. A spam [2] is an unwanted message or mail that should be avoided regarding e-commerce sites. Because of the spam the performance of system could be ruined and thereby it affects the accuracy of the system. Users when online shopping must spend their quality time to read the existing product reviews. Survey related to Yelp.com website has shown the result that more than 80 % of users look into existing reviews for making a decision to buy the product or not. More than 50% of users check the product ratings before they want to buy. More than 30% of users do comparison between one product reviews with another product reviews to get good knowledge about product [3]. Buyer can give valuable suggestions through blogs, websites and discussion boards. So these are called important source of textual data. People are giving much importance to reviews which are available online. So people overall judgment of product by examining reviews of those product [8]. When customer is getting honest impression on particular product then there is lots of chances to buy the product. Usually positive impression makes to buy the product and negative impression makes bad opinion of product. Some spammers keeping giving negative reviews to damage the quality. So we consider these is the major issue in existing system. Currently, customers give a feedback in text format, negative feedback will discourage some products, services, organizations, individuals, and even ideas. 3. CHALLENGING SYSTEM Most people review a product before spending their valuable money to buy the product. Even though they look for reviews across different websites, they could not be able to identify whether it s a spam review or not. Some companies with their Social Media Optimization team [5] add some good reviews by themselves in order to make the product famous. They give good reviews for different products manufactured by them. So the user will not able to find out whether the review is genuine one. To differentiate the spam review from genuine ones [13] in e-commerce websites, a spam review detection system is being introduced in this paper. Our system differentiate the spam reviews made by the social media optimization team based on the IP address (whose IP Address is captured?)[15].to solve this problem the system uses Naïve editor@iaeme.com

3 Spam Review Detection on E-Commerce Sites Bayesian classifier to classify between the spam review and non-spam review. The Naïve Bayesian is one of the most popular and simple methods for classifying spam data. Training the large set of data can be easily done with Naïve Bayesian classifier which takes very less time compare to other classification techniques and also accuracy of system will be increased by Naïve Bayesian Classifier [1]. In this work the system uses feature extraction technique for providing efficient data set. Feature extraction is used when the input is large and it is redundant in nature so feature is extracted to obtain better result. In this work the system uses word - count algorithm for extracting feature from dataset. Here we use web example [16] to run a sample dataset and classifying spam or non-spam. 4. THE FEATURE EXTRACTION METHOD The word-count algorithm is very simple and easy to implement to get a flexible result. With the help of this algorithm we pre-process the dataset and remove the stop-words and non-words in dataset and it counts the total number of unique words out of the total word form dataset. It also finds the frequency of that word in a particular document. The main aim of this algorithm is to make a dictionary in which the path of the preprocessed file is stored. By the way redundancy problem will be removed [2]. Here counting the word and store the frequency of that word is very helpful to find out the unique word in dataset. Figure 1 processing words, training, testing and classifications Algorithm Step1: We have to choose the file from dataset Step2: Apply Pre-processing technique to the file and remove the stop-words. Step3: Count the total word present in file and find the unique word of the file. Step4: Calculate the rate at which number of words occurs in the file. Step5: Create a dictionary and store the file path. Step6: Extracted feature from dataset editor@iaeme.com

4 Nandhini.G, T. Muthamilselvan, I. Alagiri and P. Thanapal Applying Bayesian Classifier Figure 2 challenging system Challenging system algorithm Step1: File selection Step2: Use word count algorithm and tokenization for feature extraction Step3: Train dataset using Naïve Bayesian Classifier. Step4: Calculate the probability of spam and non-spam review Probability of spam review = (sum (train matrix (spam indices)) +1) / (spam word count + number of tokens) Probability of non-spam review = (sum (train matrix (non-spam indices)) +1) / (non-spam word count +number of tokens) Step5: Trained dataset will be testing here. log _ spam review = test matrix*(log (probability of spam tokens))' + log (probability of spam review) log _ non-spam review = test matrix*(log (probability of non-spam tokens))'+ log (1 probability of spam review) If output = log_ spam review > log_ non-spam review.here spam greater than non-spam. It is classified into spam review. If non-spam are greater than the spam then it is classified into non-spam review. Step6: the result of spam and non-spam Step7: Calculate error of test data and find out the word which is wrongly classified Number of words wrong=sum (xor (output, text labels)) Step8: View the error of text data and calculate the fraction of word which is wrongly classified. Fraction of words wrong=numdocs_wrong/numtest_docs editor@iaeme.com

5 Spam Review Detection on E-Commerce Sites Naïve Bayesian Using Text Classification Ex. Product reviews Two classes: non-spam_ review and spam_ review Training Data Non-spam _review t1 : nice t2 : very nice Spam _review t3: poor t4: very poor t5: very poor, very poor. Test Data t6: nice! very nice!! poor. Calculation of non-spam_review and spam_review Prob(non-spam_review) = N non-spam_review /(N non-spam_review +N spam_ review) Prob(non-spam_review),finding the probability non-spam review. Prob(spam_ review)= N spam_ review /(N non-spam_review + N spam_ review ) Prob(spam_ review),finding the probability of spam review. Prob(non-spam_review)= N non-spam_review /(N non-spam_review +N spam_ review) = 2 /(2+3) = 0.40 Prob(spam_ review) = Nspam_review /(N non-spam_review + N spam_ review ) = 3/ (2+3) = 0.60 Prob(very non-spam_review) =(Tvery non-spam_review +1)/((Tvery non-spam_review +1)+(Tnice non-spam_review +1)+(Tpoor non-spam_review +1) = (1+1) / ((1+1)+(2+1)+(0+1)) = 2 / (2+3+1) = 0.33 Prob(nice non-spam_review) = (Tnice non-spam_review +1)/(Tvery non-spam_review +1)+(Tnice non-spam_review +1)+(Tpoor non-spam_review +1) = (2+1) / ((1+1)+(2+1)+(0+1)) =3 / (2+3+1) =0.50 Prob(poor non-spam_review) =(Tpoor non-spam_review +1)/((Tvery non-spam_review +1)+(Tnice non-spam_review +1)+(Tpoor non-spam_review +1)) =(0+1)/((1+1)+(2+1)+(0+1)) =1/(2+3+1) = 0.17 Prob(very spam_ review) editor@iaeme.com

6 Nandhini.G, T. Muthamilselvan, I. Alagiri and P. Thanapal = (Tvery spam_ review +1)/((Tvery spam_ review +1)+(Tnice spam_ review +1)+(Tpoor spam_ review +1) = (3+1)/((3+1)+(0+1)+4+1)) =4/(4+1+5) =0.4 Prob(nice spam_ review) = (Tnice spam_ review +1)/((Tvery spam_ review +1)+(Tnice spam_ review +1)(Tpoor spam_ review +1)) = (0+1)/ ((3+1)+(0+1)+(4+1)) = 1/(4+1+5) = 0.1 Prob(poor spam_ review) = (Tpoor spam_ review +1)/ ((Tvery spam_ review +1) + (Tnice spam_ review +1)+(Tpoor spam_ review +1) = (4+1) / ((3+1) + (0+1)+ (4+1)) 0.50 Prob(t6 non-spam_review) =Prob(nice non-spam_review)*prob(very non-spam_review)*prob(nice nonspam_review)*p(poor non-spam_review) = 0.50*0.33*0.50*0.17 =0.014 Prob(t6 spam_ review) =Prob(nice spam_ review)*prob(very spam_ review)*prob(nice spam_ review)*prob(poor spam_ review) = 0.10*0.40*0.10*0.50 =0.002 Prob(non-spam_review t6) =Prob(t6 non-spam_review)*prob(non-spam_review)/p(t6) = 0.014*0.40/Prob(t6) =0.005s/Prob(t6) Prob(spam_ review t6) =Prob(t6 spam_ review)*prob(spam_ review)/p(t6) = 0.002*0.60/Prob(t6) =0.001/Prob(t6) Since Prob (non-spam_ review t6)> Prob (spam_ review t6) Then t6 belongs to non-spam_ review class editor@iaeme.com

7 Spam Review Detection on E-Commerce Sites Results of non-spam_ review and spam_ review in chart based on above calculation In graphs we mentioned spam and ham. Spam is spam_ review. Ham is non-spam review. 5. CONCLUSIONS AND FUTURE WORK In this paper our main goal is to detect spam review in e-commerce sites. Many researchers have been working to find out the best classifiers. So there is a need to develop more robust classifiers to filter spam reviews.in this paper we have covered machine language techniques and approaches that have been proposed for the detection of spam reviews.. Here we apply the classifier over dataset to analyze the result. We analyzed with feature selection process which performs well with classification process and accuracy can be improved. We use Naïve Bayesian classifier which has very low error rate. To avoid unauthorized user, the system will editor@iaeme.com

8 Nandhini.G, T. Muthamilselvan, I. Alagiri and P. Thanapal ask the user to login with face book or gmail account. In that we can easily identify user details and also it helps to find out fake reviewers. REFERENCES [1] Sharma K. and Jatana N. (2014) Bayesian Spam Classification: Time Efficient Radix Encoded Fragmented Database Approach IEEE 2014 pp [2] Priyanka Sao, Pro. Kare Prashanthi, Spam Classification Using Naïve Bayesian Classifier.6, June [3] Fake Product Review Monitoring and Removal for Genuine Online Product Reviews Using Opinion Mining, Volume 7, Issue 1, January [4] Sharma A. and Anchal (2014), "SMS Spam Detection Using Neural Network Classifier", ISSN: X Volume 4, Issue 6, June 2014, pp [5] Saeedreza Shehnepoor, Mostafa Salehi, A Network-Based Spam Detection Framework for Reviews in Online Social Media, vol. 12, no. 7, July [6] Hao, Fengjun Li, Hyunjin Seo, and Roseann Pluretti, Trust-Aware Review Spam Detection, [7] N. Jindal and B. Liu. Review spam detection. In WWW (poster), [8] J. Liu, Y. Cao, C.-Y. Lin, Y. Huang, and M. Zhou. Low-quality product review detection in opinion Summarization. In EMNLP-CoNLL, [9] Sharma A. and Anchal (2014), "SMS Spam Detection Using Neural Network Classifier", ISSN: X Volume 4, Issue 6, June 2014, pp [10] Panigrahi P. (2012), "A Comparative Study of Supervised Machine Learning Techniques for Spam Filtering", Fourth International Conference on Computational Intelligence and Communication Networks, IEEE 2012, pp [11] Guzella, T. S. and Caminhas, W. M. A review of machine learning approaches to Spam Filtering. Expert Syst. Appl., 2009 [12] Wu, C. Behavior-based spam detection using a hybrid method of rule-based techniques and Neural networks Expert Syst., 2009 [13] Khorsi. An overview of content-based spam filtering techniques, Informatics, 2007 [14] Clark J. et al (2010), A Neural Network Based Approach to Automated Tweets Classification. [15] Nitin Rola and Prof. Rashmi Gupta, Prepare Black List Using Bayesian Approach To Improve Performance Of Spam Filter, Volume 4, Issue 1, January- February (2013), pp , International Journal of Computer Engineering and Technology (IJCET). [16] R. Manickam, D. Boominath and V. Bhuvaneswari, An Analysis of Data Mining: Past, Present and Future, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 1, 2012, pp. 1-9, ISSN Print: , ISSN Online: [17] Gopi Sanghani and Dr. Ketan Kotecha, Support Vector Machine For Personalized Spam Filtering. International Journal of Advanced Research in Engineering and Technology, 8(6), 2017, pp [18] Birru Devender1,Korra Srinivas2,Ch.Tulasi Ratna Mani, Detecting Spam Zombies By Monitoring Outgoing Messages, Volume 5, Issue 5 (May 2016), PP [19] editor@iaeme.com