Feedbacks as knowledge source: machine learning possibilities for analyzing customer opinions

In the context of digitalization and globalization of the hotel market, customer reviews become especially important, having a significant impact on the hotel reputation and its competitiveness. This paper explores the possibilities of using machine learning methods to automate the analysis of customer opinions, which are becoming an important tool for business strategies in the hospitality industry. Traditional practices of collecting and analyzing feedback are not always able to cope with the volume of information, which opens up new horizons for natural language processing and deep learning technologies. The authors emphasize that positive feedback significantly increases the level of trust of potential customers, forming their expectations. Based on an in-depth analysis of thematic patterns and the tone of reviews, hotels can not only identify weaknesses in service, but also develop effective marketing and service strategies. The paper also discusses modern approaches to automating feedback analysis, including classification algorithms, neural networks and sentiment analysis, which have shown their effectiveness in making operational business decisions. In conclusion, it is emphasized that the integration of machine learning methods into the hotel quality and reputation management processes not only contributes to improving the service level, but also allows a deeper understanding of consumer preferences. In the future, it can become a fundamental factor for the successful adaptation of business strategies in highly competitive conditions. Thus, the technology use for analyzing feedbacks is a critical area for the sustainable hotel business growth in the future.