Machine Learning Techniques for Hotel Online Reputation
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 Ngineering, Dehradun, India

Manuscript received on 05 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 July 2019 | PP: 3004-3007 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8004078919/19©BEIESP | DOI: 10.35940/ijitee.I8004.078919

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Abstract: 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.
Keywords: Unsupervised Machine Learning Technique, Text Mining, Supervised Machine Learning Technique, Semi-Supervised Machine Learning technique, Reinforcement Machine Learning Technique, Hype, Quantification, Collision, Manipulation, Machine Learning, Mining, Deep Learning etc

Scope of the Article: Machine Learning