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Review of Fake Product Review Detection Techniques

EasyChair Preprint no. 7224

6 pagesDate: December 17, 2021


In the present-day world, reviews available on online websites play a vital role in the sale of products and resources. People using an online platform for purchasing different products very often read reviews of the product before making a decision on which type of product to which platform to buy and whether to buy or not. Basically, reviews are the statements that express the opinions, suggestions, or experiences of a person about any product available in the market. These reviews have their impact directly on the manufacturers and retailers as they are highly concerned about the feedback and opinion received by the consumer because customer service is the key. But nowadays spam/fake/pseudo reviews are written deliberately on the products to build/break their reputation and attract more consumers. This practice of giving falls truth untruthful deceptive reviews on the products to promote or devaluate the commodities and services is called opinion spam or review spam. Because of these reasons, it is the need of the hour to bring in new techniques for solving the problem of detecting fake reviews. With respect to this, our review paper compares the performance of different techniques Sentimental Analysis, different Boosting techniques in ML.

Keyphrases: Machine Learning Techniques, Opinion Mining, opinion spam, Sentiment Analysis, XGBoost techniques

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {B V Santhosh Krishna and Sanjeev Sharma and Y Sahana and K Devika and K N Sharanya and K Indraja},
  title = {Review of Fake Product Review Detection Techniques},
  howpublished = {EasyChair Preprint no. 7224},

  year = {EasyChair, 2021}}
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