Restaurant Recommender System based on Sentimental Analysis

Recommendation systems are being enforced to offer personalized set of services to the users. They are basically build to produce recommendations or suggestions (like restaurants, places...) that comply with user's concern and that can be applied to multiple fields. To enhance the quality and service of Recommendation systems and to resolve any issues related to it, various effective techniques linked to data management can be made use of. The current paper proposes a machine learning algorithm to resolve the issue of personalized Restaurant selection relying upon tripadvisor.com search data. The facilities provided by the hotel along with user's comments are being utilized. The NLP - Natural Language Processing is imbibed for examining and tagging all the previous user's comments (whether positive or negative) for every hotel, thereafter computing the overall % of the comments and storing the output. In the process of Restaurant recommendation, first the user chooses the hotel's features according to his interest and centered on this, the corresponding hotels are fetched and the user comments are examined to identify the hotel with the highest ranking. Eventually, the highest rated hotel is being recommended to the user by the restaurant recommended system.