Fuzzy Logic In The Digital Travel Era: Enhancing Tourist Experience Through Comprehensive Hotel Reviews And Sentiment Analysis

Abstract

In today's society, social media is becoming a more and more important part of our everyday lives as it continues to evolve. Lot of of users assess and tourist rate attractions daily, and hotels play a significant part in travel. Following reviews can be implicated to fuzzy logic, which will be beneficial for resolving the fame of tourist attractions and hotel reviews. Tourists can choose their accommodation and travel destinations with ease based on the outcome of the fuzzy control system. In this research, fuzzy logic has been used to implement sentiment analysis. To provide precise pass on relating to a few aspects chosen by the end user, the system uses attributes including food quality, service, locations, and ratings. This method enables customers to assess a certain hotel service's quality using a variety of factors. Additionally, a user can compare various hotels in depth. To accomplish this, a membership task that specifies how a smooth value maps to the term on a scale of 0 to 1 is used in the proposed system of fuzzy logic. It essentially outlines "how good" something is and how to produce complex behaviour from a condensed, understandable set of professional guidelines before passing these variables to a fuzzy control system. Using a set of rules, a fuzzy control system connects fuzzy variables. These laws are merely mappings that explain the relationships between one or more fuzzy variables. After specifying the inputs and obtaining the priority of the alternatives, the control system is finally simulated. As food preferences, service quality, and user-specific amounts vary, the system is scaled for each individual user. As a result, the system offers more accurate outcomes and increased classification method accuracy for the supplied review data set. Future research will use the Trapezoidal Fuzzy Number (TpFNs) as an evaluation tool to reflect clearly the variation of an element from 0 to 1 in order to increase the model's accuracy.