Spam Reviews and Spammer Community Detection using Machine Learning Algorithms

Authors

  • Nishigandha Rananaware  Department of Computer Engineering, NESGI, Pune, India
  • Shweta More  Department of Computer Engineering, NESGI, Pune, India
  • Prajakta Jagtap  Department of Computer Engineering, NESGI, Pune, India
  • Anuja Kumbharkar  Department of Computer Engineering, NESGI, Pune, India
  • A. C. Jadhav  Assistant Professor, Department of Computer Engineering, NESGI, Pune, India

Keywords:

CNN, spam reviews, machine learning, social network

Abstract

Online reviews and feedback of a product plays a vital role in human tendency to purchase those products. To affect the product sale spammer generates fake reviews on online social media platform. To identify spam reviews and spammer communities is the area of interest of this research work. In literature work, various spam detection techniques are proposed based on Review-Behavioral (RB) Based features, Review-Linguistic (RL) Based Features, User-Behavioral (UB) Based Features are explained but none of the technique provide a simultaneous study of these features and weighting of the features along with finding the relationship among the spam users. The proposed work generates a hybrid feature selection method which merge linguistic based features and behavioral features along with NLP processing and sentiment analysis. Also deep learning classification is used. The results show 91 % accuracy for detecting spam reviews.

References

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2021-06-30

Issue

Volume 8, Issue 3, May-June-2021

Section

Research Articles

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Copyright (c) IJSRSET

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Nishigandha Rananaware, Shweta More, Prajakta Jagtap, Anuja Kumbharkar, A. C. Jadhav "Spam Reviews and Spammer Community Detection using Machine Learning Algorithms" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 8, Issue 3, pp.444-449, May-June-2021.