Unlocking the Potential of Novel LSTM in Airline Recommendation Prediction

Future customers need customer feedback to learn from their previous experience about the goods offered by an organization. Additionally, customer feedback and ratings assist businesses in enhancing operations and developing fresh approaches to offering excellent services. This study focuses on consumer ratings and reviews to look at the relationship between the product a customer ranks and their recommendations. In two components, this work forecasts user recommendations. The preliminary phase of the research utilizes the LSTM model for the sentiment analysis. This procedure is undertaken to ascertain the probability of a customer harboring a specific sentiment towards the services provided by the airline. The ensuing module exclusively evaluates varied customer service aspect ratings across a range of airline services. Moreover, our recommended ensemble methodology can provide benefits to professionals who seek to integrate user-generated evaluations and appraisals. By utilizing this approach, those who wish to incorporate customer-generated reviews and ratings can gain a clear and comprehensive understanding, which can assist in improving service delivery, post-purchase planning, and strategy development. By integrating an aggregated assessment of the service's efficacy, this suggested technique might also be helpful to forthcoming passengers.