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An Approach to Improve the Accuracy of Detecting Spam in Online Reviews

EasyChair Preprint no. 4709

4 pagesDate: December 7, 2020


Customers or user opinion is most important and valuable information at online now-a-days especially in product reviews. Mostly customer used to make their decision for purchasing a particular product according to the other’s customer reviews. Those reviews are increasing the rating of that e-commerce site. Normally reviews are considered unbiased opinion of a person whose have personal experience with a related specific product. The noticeable thing is many reviews are not real or authentic. These kinds of reviews are usually called spam and it is becoming a large problem an online and others electronic communication. For the increasing of the value of online review, the spammers are getting inspire to doing spam for them or promoting a specific e-commerce website. Also, they are demoting a specific site for payment. In this paper we have discussed about some traditional techniques for detecting spam in online public opinions. Next, we have used stacking algorithm with some traditional classifiers for the detection of spam reviews. Finally, performance of different classifiers has been evaluated through simulation experiment. From the experiment we have seen that stacking classifier provides better accuracy than other traditional classifiers.

Keyphrases: Detecting Spam, online reviews, Spam in Reviews, Stacking Classifier

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Abida Khanam Suborna and Suman Saha and Chironjit Roy and Md. Tojammal Haque Siddique and Shuvrodeb Sarkar},
  title = {An Approach to Improve the Accuracy of Detecting Spam in Online Reviews},
  howpublished = {EasyChair Preprint no. 4709},

  year = {EasyChair, 2020}}
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