A Study On Online Spam Review Detection Methods By Machine Learning Approach
Main Article Content
Abstract
Online reviews for products, movies, shopping, etc. have become the major purchase decision of customers to make before buying the product. These reviews are kind of public opinion about the product which they bought and this may help another person who is willing to buy the product. The impact of reviews had made manufacturers, and retailers to be more concerned about making the product as a best one. Many retailers such as Reliance, Big Bazaar, More, etc are very concerned about online reviews and there are possibilities for these reviews to affect business either positively or negatively. Certain retailers are trying to create false reviews about the product through AI as a part of promoting the product. This process is termed the Opinion spam or opinion review spam here is where the reviewers manipulate a wrong review for selling the product for profit. Not all online reviews are trustworthy and truthful so in such cases, a model must be created for detecting online review spam. This research paper tries to highlight the online spam review detecting by machine learning approach using NLP (Natural Language Processing) where the extracted features from the text will be taken for further review. In this research main focus was shifted to the ML technique for detecting review spam, and classifying them. The main aim of the research is to provide a comprehensive ad strong ML approach for detecting review spam.
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.