Major Society of people using internet trust the contents of internet. The possibility that anyone can take survey about anything provides a great opportunity to spammers to make fake reviews about product, its services. So to identify the spammers and their spams is really a debatable issue for research and despite that there are many studies in this context yet none of them has a great significance. In this application, we use a structure, stated as NetSpam, which proposes spam features for giving a practical hotel review datasets to design a spam review detection method into a classification issue. Utilizing the role of spam features helps us to have good outcome in context of different metrics on review datasets. The results shows that NetSpam outcome with the previous methods and encompassed by features of the four categories; involving behavior and language of review feature, behavior and language of user feature, better outcomes can be obtained from first type of feature rather than other ones. The contribution work is when user will search query it will display all top products as well as there is recommendation of the product.
Machine learning algorithms
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL
₹10000 (INR)
2020