Online Hotel Fake Review Detection – Machine Learning Project

Abstract 

Todays we difficult find Online Hotel Fake Review Detection, some people gives the fake reviews on hotel to broke down the hotel rating. So Individuals use online reviews to make decisions about available products and services.

In recent years, businesses and the research community have shown a great amount of interest in the identification of fake online reviews. Applying accurate algorithms to detect fake  online reviews  can protect  individuals from spam and misinformation.

We gathered filtered and unfiltered online reviews for several hotels in the Charleston area from yelp.com. We extracted part-of-speech features  from the  data set,  applied three  classification  models,  and  compared  accuracy  results  to related works.

Introduction to Online Hotel Fake Review Detection – Machine Learning Project

There are thousands of reviews online, which makes it convenient for people to make decisions, but the amount of data makes it difficult to sort through .

The real value of online reviews is in its content and the certainty that reviewer indeed received products or services prior to writing the review. Promotion or demotion of the products and services is one of the main reasons for deceptive reviews.

At  times, to create better ratings for the venue, hotel owners pay employees to fabricate false reviews . Alternatively, some reviewers write negative reviews for malicious reasons, like to distort the reputation of the business reviewed .

Yelp.com is one of the biggest online review sites.So that we implement this project to find the fake reviews using machine learing algorithm in SVM.

Accuracy results using human judges was reported at 61.9%, using bigram features 89.6%, and using psycholinguistic deception detection approach 76.8%.

System Architecture to Online Hotel Fake Review Detection – Machine Learning Project

H/w and S/W requirements

Hardware:

Computer   :   System.

Ram           :    1GB

Rom           :    32GB

Software:

Technology    :    Machine Learning.

Front End          :     GUI-tkinter.

IDLE                   :      python 3.10.4

Virtual  Envs     :     Anaconda