Fake review detection from ecommerce website is a necessary research in this internet world.
INNOVATION IDEA OF THE PROJECT
The innovation of this project is analyzing the fake review problem in the consumer product field, more precisely studying Yelp businesses from four of the biggest cities of the USA. No prior research has been carried out in this concrete field, being restaurants and hotels the most previously studied cases. We want to prove that fake review detection problem in online consumer retailers can be solved by machine learning means and to show if the difficulty of achieving it depends on geographic location.
PURPOSE OF THE PROJECT
One of the main problems about opinion sharing websites is that spammers can easily create hype about the particular product by writing spam reviews. These spam reviews may play a key role in increasing the value of the product or service. For instance, if a customer wants to purchase any product online, they usually go to the review section to know about other buyers’ feedback. If the reviews are mostly positive, the user may buy, otherwise, they would not buy that specific product. This all shows that spam reviews have become the main problem in online shopping that may lead to a loss for both the customer and the manufacturer. The purpose of the project is identify such as spamming or fake reviews.
SCOPE OF THE PROJECT
Review spam can financially affect businesses and might cause a sense of mistrust in the general public, therefore, due to its significance, this problem has recently attracted the consideration of the media and governments as well. Hence, detecting spam reviews appears to be a key area, and without solving this important issue, online review sites could become a place full of lies and completely useless.
PROPOSED SYSTEM
The proposed system studies the spam or fake review classification from the customer review in e-commerce website like yelp.
The proposed system uses feature extraction technique on yelp dataset.
Machine learning approach used to achieve fake review classification.
Benefits of proposed system
Fake review identification with high level feature extraction techniques
Achieve high accuracy on fake reviews
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