After revolution in cell phone industry expansion and offering of promotional data packs by telecom companies like Reliance Jio, Airtel, Idea, Spice etc accessibility to the Internet has become very easy for the people. maximum people are now connected through social media viz. facebook, twitter, instagram etc. People are sharing their best and worst experiences for any brand. Various online review sites like Treebo, Yelp, Google Maps, and Tripadvisor OYO, Makemytrip, goibibo etc are used as an important source for the success of hotel businesses. Word of mouth has always been a powerful tool for marketing a business, Online reviews are today’s word of mouth marketing, and these can make or break your business; In this research paper it is proposed for analyzing online reviews about hotels our algorithm must able to detect and analyzing fake reviewers based on user, tweet, timestamp, IP, collision and manipulation concept as well as to develop optimal model (based on group theory) for detecting fake reviewers, Improvement in enhancing sentimental analysis and the review detection model which can be implemented on all positive or all negative reviews, also the algorithm must able to identify the best fit of four machine learning techniques: (supervised machine technique technique, text mining technique , support vector machine learning technique and Naïve bayes machine learning technique) for specify and verify the different parameters of classification of reviews. Algorithm must able to Quantify the results of above techniques and extract the parameters to analyze the Genuinity of reviews based on Location, Security, Price, Quality, Ambiance etc.

Now days when someone decide to book a hotel, previous online reviews of the hotels play a major role in determining the best hotel within the budget of the customer. Previous Online reviews are the most important motivation for the information that are used to analyse public opinion. Because of the high impact of the reviews on business, hotel owners are always highly concerned and focused about the customer feedback and past online reviews. But all reviews are not true and trustworthy, sometime few people may intentionally generate the fake reviews to make some hotel famous of to defame. Therefore it is essential to develop and propose the techniques for analysis of reviews. With the help of various machine learning techniques viz. Supervised machine learning technique, Text mining, Unsupervised machine learning technique, Semi-supervised learning, Reinforcement learning etc we may detect the fake reviews. This paper gives some notions of using machine learning techniques in analysis of past online reviews of hotels, Based on the observation it also suggest the optimal machine learning technique for a particular situation