The tourism industry has experienced substantial growth in recent years, propelled by the evolving customer preferences and pervasive influence of social media platforms. To gain a competitive advantage, it is imperative to develop innovative approaches that enable the delivery of personalized services and a deeper understanding of customer satisfaction. By leveraging user-generated content, firms can better understand dynamic customer trends and optimize their marketing strategies accordingly. However, our research aims to develop a novel customer segmentation framework using advanced deep learning techniques to analyze user-generated content within the rapidly growing tourism industry. The methodology employs a novel hybrid deep learning approach that integrates Sentence-BERT for contextualized embeddings and a GRU-based Autoencoder for feature reduction in an adaptive manner. This process is followed by k-means clustering, which segments customers using both multi-criteria ratings and online reviews to provide a holistic understanding of customer experiences. The proposed framework's effectiveness is compared with multiple state-of-the-art models using robust evaluation metrics such as the silhouette coefficient and the Davies–Bouldin index. The study’s findings highlight that the proposed model classifies customers into four distinct segments, and the statistical test performed shows the significance of our result. The research study contributes to the advancement of user-generated content based market segmentation in the tourism industry by unifying textual and numerical feedback into a single analytical framework. The findings offer valuable insights for tourism businesses seeking to enhance customer satisfaction through personalized service strategies and data-driven decision-making.

The sudden surge of digitalization escalates the challenges faced by traditional tax systems to detect and combat tax evasion, and it is a pivotal concern for the smooth functioning and sustainable development of any nation. The paradigm shift offered by the emergence of new-age technologies presents unprecedented opportunities to tap their potential for administering effective tax systems. In our paper, we provide a systematic scientometric analysis of existing literature to analyze four focal new-age technologies in combating tax evasion. We also propose a novel holistic framework to understand the intricacies of this multifaceted landscape of tax evasion from technological, ethical, legal, social, and economic (TELSE) perspectives. The research methodology gives a quantitative scientific mapping to analyze research publications from Web of Science and Scopus databases using Biblioshiny. A total of 117 documents were examined, spanning over the last decade (2014-2024). The research findings highlight considerable traction regarding the number of publications from the two most populated countries in the world. The analysis of the most frequent keywords yields an increasing trend towards the adoption of other new-age technologies as well and depicts different factors that affect tax evasion, which is in line with varied laws and regulations across countries. The interdisciplinary research efforts need to be aligned to tap the full potential of these technologies and to develop effective intelligent taxation systems that are fair, accountable, and explainable.