This study highlights the importance of managing and analyzing customer reviews to gain a competitive advantage and improve customer experience in the hospitality industry. In this context, a deep learning-based sentiment analysis system of hotel customer reviews is developed to evaluate service quality within the scope of sustainable tourism. The study analyzed 15,522 customer reviews of five-star hotels in Antalya using text mining, topic modelling, and deep learning-based sentiment analysis. The reviews were classified as positive, negative, or neutral. The findings show that Hotel HB2 has the highest performance, with an F1 score of 97.9%. Overall customer satisfaction is 91%, while emotional satisfaction stands at 77%. Key factors, such as cleanliness, food quality, and staff professionalism, were found to play a critical role in customer loyalty. Additionally, this study integrates sustainability-orientated themes by identifying customer feedback related to environmentally friendly practices and sustainable hotel operations. The results provide evidence that customer satisfaction is not only influenced by service quality but also by the perceived environmental and social responsibility of the hotel. Machine learning techniques have emerged as effective tools for analyzing large-scale customer reviews, offering valuable insights to rapidly and accurately capture customers’ emotions, expectations, and perceptions. As a comprehensive application of sentiment analysis and text mining, this research offers hotel managers a practical framework to enhance service quality, foster customer loyalty, and develop sustainability-orientated strategies. This study contributes to the literature by linking AI-driven sentiment analysis with sustainability practices in the tourism sector.

The main purpose of this study was to reveal the relationships between green purchasing intention, sustainability consciousness, environmental attitude, and ecotourism experience within the scope of the theory of planned behaviour. The results of the analysis showed that there are positive relationships between sustainability consciousness, environmental attitude, ecotourism experience, and green purchasing intention. The model was tested using SPSS with 435 survey responses. Tourists with a higher consciousness of sustainability develop positive attitudes toward the environment. Tourists with positive attitudes toward the environment intend to purchase more green products. However, while tourists with more ecotourism experience have stronger green purchasing intentions, less ecotourism experience does not affect green purchasing intentions. This study highlights the critical role of sustainability consciousness in shaping environmental attitudes and behaviours.

Sustainability consciousness (SC) is a crucial determinant for the successful execution of sustainability initiatives. The changing of citizens’ knowledge, attitudes, and behaviors is an essential component in attaining sustainable development (SD). Ensuring a sustainable future hinges on cultivating a durable sense of awareness among citizens. The aim of this article is to present a systematic literature review (SLR) on SC and to reveal the research trends and future perspectives. The data were obtained from the Web of Science (WoS) database and the data analysis of 49 articles selected based on determined criteria was carried out with two approaches: bibliometric and content analysis. The analysis reveals that publications pertaining to the topic of SC have emerged mainly within the past decade, with a maximum annual publication count of nine. The analyzed publications were categorized into four primary themes: (1) SC through education for sustainable development (ESD), (2) SC of consumers, (3) SC in business, and (4) measurement tools for SC. Education and educational research emerged as the primary field of study, with students being selected as the focus of research. However, research has indicated that even among children who receive SC education, there may be a decline in SC levels over a period. The attainment of the United Nations’ Sustainable Development Goals (SDGs) necessitates a society characterized by a high level of SC. The scope of SC should extend beyond educational institutions to encompass broader societal contexts, including corporations and professional environments. The findings of this study demonstrate significant deficiencies in the existing literature on SC and emphasize the significance of SC in attaining the UN SDGs.

This study aims to analyse consumer experiences, purchase behaviours, and emotional responses through Booking and Expedia’s mobile applications. The 2000 user reviews collected from Google Play were subjected to a comprehensive sentiment analysis, text mining, and topic modelling process to identify the key elements that shape consumers’ emotional experiences and purchase decisions. According to the results of text mining and sentiment analysis performed with Python’s WordNet library, 81.9% of Booking.com reviews are positive, 8.4% are negative, and 11.3% are neutral, whereas 55.8% of Expedia reviews are positive, 37.8% are negative, and 8.0% are neutral. In the topic modelling analysis, Booking.com emphasised ease of booking, while Expedia emphasised difficulties in cancellation and refund processes. These findings provide valuable insights into how consumers’ emotional states and purchasing behaviours are reflected in their experiences with mobile applications. The study enables the development of strategic recommendations for marketing management to better analyse consumers’ expectations and experiences.

In this study, the estimation of high-tech exports for Turkey’s foreign trade target in line with sustainable development was carried out. The research was carried out for Turkey since it has been focusing on sustainable and environmentally friendly production and an export-oriented growth model, with a transformation in its economic growth strategy as of 2021, and high-tech products are a determining factor in the export target. In this research, three different machine learning techniques, namely artificial neural networks, logistic regression, and support vector regression, were used to determine a successful prediction method close to the ideal scenario. In the models, high technology exports for the period of 2007–2023 with data obtained from the World Bank were taken as the dependent variable, while the gross national product, number of patents, and research and development expenditures were taken as independent variables. By calculating the R², MAPE, and MSE metrics, the success of the model with the least error was evaluated, and it was seen that artificial neural networks (ANNs) were the most successful model, with values of 94.2%, 0.011, and 0.073, respectively. The ANN model was followed by support regression and logistic regression.