<p>Modern approaches to mining massive volumes of review data for actionable insights are required to keep up with the ever-increasing volume of user-generated content and the burgeoning tourist industry. The nuance and complexity of human emotions are difficult for traditional methods of review analysis to capture. User reviews are one of the quality and service-improving ways in the tourism industry that express the experience and recommendations from travellers. Analysing such review data improves emotion analysis and public/private service industry hospitality management. This article introduces a Hybrid Review Analytical Method (HRAM) by assimilating the conventional short-term memory and fuzzy control algorithm. This method analyses positive and negative reviews using Long Short-Term Memory (LSTM) under repeated traveller presence. The user’s presence under different review fields is analysed using their interconnected dependencies. The reviews are validated for one-time service validation if the dependencies are unavailable. The fuzzy process is responsible for extracting the type of emotion (such as joy, anger, happiness, etc.) for further dependency estimation. The varying emotion dependencies are, thus, identified using the fuzzy process for augmenting LSTM validations. This process is recurrent until multiple emotions are analysed using the dependencies and non-dependencies across various user reviews under different tourism services. Improving the emotional analysis of travel evaluations is the goal of this study, which suggests combining LSTM networks with a fuzzy control algorithm. Together, the LSTM’s strength in sequential data processing and the fuzzy logic system’s capacity to handle imprecise inputs provide a sentiment analysis tool that is both more accurate and more nuanced. Improving customer services and experiences is the goal of this innovative strategy, which seeks to change how players in the tourist sector understand and respond to consumer input.</p>