Pankaj Chauhdary, Dr. Anurag Aeron, Dr. Sandeep Vijay
Abstract: Background: Since previous decades Internet as well as smart phones have become easily accessible to maximum people. This has made social networking an integral part of human life. People are sharing their comments and reviews on the forum or portal about their views and experiences. These reviews help others to judge the brand value of any product. Even in taking the final decisions about the brand selections for best hotels, colleges and products people are gradually depending on the previous online reviews. In such scenario, some companies may indulge themselves in generating the fake reviews with wrong intentions to create the positive or negative hype about the particular products. It may mislead the customers and decision makers. Objectives: Objective is to develop an algorithm to development of the optimal machine learning algorithm for hotel reviews Efforts are made to remove maximum limitations and constraints of existing algorithms to develop a robust algorithm. Methodology: After finding the gaps appropriate mathematical models are proposed to be implemented to detect genuinety of the reviews based on behavior metrics, quantify the past trust analysis of the reviewer, group membership activities and quantify the sentimental analysis for the hotels. Findings: Due to filtration of the spam reviews and fake reviewers, systematic predication about the hotel facilities and ambience may be done that will encourage the customer to use the hotel booking website that will utilize such algorithms. Applications/Improvements: Although this work is specifically proposed for helping customers in selection of the best hotels by analyzing the previous online reviews, and help in concluding the right decision based on Location, Security, Price, Quality, Ambiance etc. Yet the something similar model may be designed after minor modifications for taking right decision in selecting the best colleges, best products etc.
Keywords: Classification, Machine learning, Burst rate, sentimental analysis, past trust analysis etc