Survey on Fake Online Reviews Using Machine Learning Algorithms
Year: 2020
Type: article
Abstract: In current days, Sentiment Analysis (SA) has become one of the prominent topics in taking decision about e-commerce products.
As this has become one of the main roles in business environments, still it is having a lot of limitations like spam users try to
inject fake online reviews which are not at all related to that exact topic. One main limitation which is faced by sentiment analysis
is unable to extract the emotions properly from the opinion like detecting fake positive reviews and fake negative reviews about
that product or post. Due to this spammersimpact, all the company manufacturers and retailers are more concerned with customer
feedback and their individual reviews. The process of converting a normal review into spam or fake review by manipulating the
original reviews into poison reviews(I.e. making fake and untruthful reviews) is known as Opinion (Review) Spam. As we all
know that almost all the online reviews may not be trusted, some of them are spam and fake reviews which are generated by the
spammer during the social communication. Hence we try to concentrate on the importance of identification of spam reviews and
try to extract the features from Natural Language Processing (NLP), it is also possible to conduct ore spam reviews based on
several machine learning algorithms. In this present paper we try to conduct a survey on several ML algorithms that are used to
solve the problem of spam reviews detection and try to see the solutions which are provided by several reviewers. In current days
there is a huge demand for sentiment analysis for fake online reviews detection using several ML algorithms. There is a lot of
research work going on to identify the fake online reviews detection, but no technique is a complete success in identifying and
preventing the fake online reviews in effective manner. In this survey thesis, we nearly provide the review of more than 10
research papers suggesting various methods adopted for the effective fake users detection using machine learning techniques. In
each and every research topic there is one part discussing more about the importance of identifying tumor in early stages and
another paper discussing the cons of the previous paper.
Source: Journal of Critical Reviews
Authors: Pilaka Anusha, Kaki Leela Prasad
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W3046232581