An Algorithm for Deciding the Online Reputation of Hotels
Pankaj Chaudhary1, Anurag Aeron2, Sandeep Vijay3
1Pankaj Chaudhary, Research Scholar, FST, ICFAI University, Dehradun, India.
2Dr. Anurag Aeron, Associate Professor, FST, ICFAI University, Dehradun, India.
3Dr. Sandeep Vijay, Director, Shivalik College of Engineering, Dehradun, India
Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 July 2019 | PP: 2998-3003 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8003078919 /19©BEIESP | DOI: 10.35940/ijitee.I8003.078919
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Hype, Quantification, Collision, Manipulation, Machine Learning, Mining. Deep Learning etc
Scope of the Article: Machine Learning