Fake Product Review Detection and Removal Using Opinion Mining Through Machine Learning

  • Minu Susan JacobEmail author
  • Selvi Rajendran
  • V. Michael Mario
  • Kavali Tejasri Sai
  • D. Logesh
Conference paper


Machine learning is one of the growing trends in artificial intelligence and deep learning scenarios where the machine learns to acquire data from previous cases and implements the data for future prediction and analysis. The objective of this chapter is the detection and removal of fake reviews in online reviews. Majority of online buyers rely on product reviews before making purchase decision of their chosen brand; however, fake reviews pose a continuous threat to the integrity of the product, portals and the easy-to-find reviews on specific products. This chapter aims to develop a system to identify and remove fake reviews with the view of protecting the interests of customers, products and e-commerce portals. Thus, in this proposal, the primary goal is detecting unfair reviews on Amazon reviews through Sentiment Analysis using supervised learning techniques in an E-commerce environment. Sentiment classification techniques are used against a dataset (Amazon) of consumer reviews for smartphone products. Precisely, we use three different algorithms, logical regression algorithm, linear regression algorithm and neural networks (CNN and RNN models), of supervised machine learning technique to find similarities in the review dataset and group similar datasets together to explore unfair and fair positive and negative reviews, which involves screening, collaborative filtering, and removing with an optimal accuracy rate. The core focus or the highlight of this chapter is to explore an algorithm using deep learning that ensures optimal accuracy in the identification of fake reviews.


Sentimental analysis Deep learning Fake review Neural networks (CNN and RNN) 



Area under the curve


Convolutional neural network


Least short-term model

ML Algo

Machine learning algorithm


Natural language processing


Recurrent neural networks


Word embedding visualization


With stop words


Without stop words


  1. 1.
    Anderson ET, Simester DI (2014) Reviews without a purchase: low ratings, loyal customers, and deception. J Mark Res 51(3):249–269CrossRefGoogle Scholar
  2. 2.
    Ott M, Cardie C, Hancock J (2012) 2012. Estimating the prevalence of deception in online review communities. WWWGoogle Scholar
  3. 3.
    Wang Z (2010) Anonymity, social image, and the competition for volunteers: a case study of the online market for reviews. BE J Econ Anal Policy 10(1):1–34Google Scholar
  4. 4.
    Heydari A, Ali Tavakoli M, Salim N, Heydari Z (2015) Detection of review spam: a survey. Expert Syst Appl 42(7):3634–3642CrossRefGoogle Scholar
  5. 5.
    Sinha A, Arora N, Singh S, Cheema M, Nazir A (2018) Fake product review monitoring using opinion mining. Int J Pure Appl Math 119(12):13203–13209Google Scholar
  6. 6.
    Elmurngi EI, Gherbi A Unfair reviews detection on Amazon reviews using sentiment analysis with supervised learning techniques. Received: 01-02-2018, Revised: 01-05-2018, Accepted: 11-05-2018Google Scholar
  7. 7.
    Li F, Huang M, Yang Y, Zhu X (2011) Learning to identify review spam. In: International joint conference on artificial intelligence, pp 2488–2493Google Scholar
  8. 8.
    Crawford M, Khoshgoftaar TM, Prusa JD, Richter AN, Najada HA (2015) Survey of review spam detection using machine identifying deceptive reviews based on labeled and unlabeled data. Ph.D. thesis, Wuhan UniversityGoogle Scholar
  9. 9.
    Asadullah SM, Viraktamath S Classification of twitter spam based on profile and message model using svmGoogle Scholar
  10. 10.
    Seneviratne S, Seneviratne A, Kaafar MA, Mahanti A, Mohapatra P (2017) Spam mobile apps: characteristics, detection, and in the wild analysis. ACM Trans Web 11(1):129CrossRefGoogle Scholar
  11. 11.
    Lupker SJ, Acha J, Davis CJ, Perea M (2012) An investigation of the role of grapheme units in word recognition. J Exp Psychol Hum Percept Perform 38(6):14911516CrossRefGoogle Scholar
  12. 12.
    Olah C Understanding LSTM networks. Retrieved from Understanding-LSTMs/

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Minu Susan Jacob
    • 1
    Email author
  • Selvi Rajendran
    • 1
  • V. Michael Mario
    • 1
  • Kavali Tejasri Sai
    • 1
  • D. Logesh
    • 1
  1. 1.KCG College of TechnologyChennaiIndia

Personalised recommendations