Author : Mrs. Sonali Ratnakar Nawale and Prof. S. M. Aher

Major Society of people using internet trust the contents of net. The liability that anyone can take off a survey give a brilliant chance to spammers to compose spam surveys about hotels and services for various interests. Recognizing these spammers and the spam content is a widely debated issue of research and in spite of the fact that an impressive number of studies have been done as of late towards this end, yet so far the procedures set forth still scarcely distinguish spam reviews, and none of them demonstrate the significance of each extracted feature type. In this application, use a novel structure, named NetSpam, which proposes spam features for demonstrating hotel review datasets as heterogeneous information networks to design spam review detection method into a classification issue in such networks. Utilizing the significance of spam features helps us to acquire better outcomes regarding different metrics on review datasets. The outcomes represent that NetSpam results with the previous methods and encompassed by four categories of features; involving review-behavioral, user-behavioral, review linguistic, user-linguistic, the first type of features performs better than the other categories. The contribution work is when user will search query it will display all top hotels as well as there is recommendation of the hotel by using user’s point of interest.

Keywords: Social Media, Spammer, Spam Review, Heterogeneous Information Networks, Sentiment Analysis, Content Similarity, Netspam