USING AI MODELS IN THE CONTENT ANALYSIS OF POTENTIAL TOURISM DEMAND

Authors

  • N.A. Dekhtyar Simon Kuznets Kharkiv National University of Economics, Department of International Economics and Management, Nauky Ave., 9A, Kharkiv, 61165, Ukraine https://orcid.org/0000-0001-7932-8620

DOI:

https://doi.org/10.15330/apred.2.22.72-88

Keywords:

Artificial intelligence, content analysis, tourism demand, large language models (LLMs), sentiment analysis, monitoring consumer demands, strategic management in tourism

Abstract

This article examines the use of modern artificial intelligence (AI) models for content analysis of potential tourism demand in the context of global digital transformation, and the need to revitalise Ukraine’s tourism sector strategically. It analyses the process of defining tasks for monitoring open web resources, and of formulating requirements for optimal large language models. This is based on monographic analysis and the synthesis and modelling of processes for generalising unstructured digital data. A key finding is the experimental comparison of extractive (BERT and SBERT) and abstractive (GPT, BART, T5 and Pegasus) models. This demonstrated that the BART model provides the most balanced combination of contextual understanding and generative fluency. Other models may, however, distort the audience’s actual sentiments due to an excessive focus on specific groups or the generation of non-existent ideas. The technological workflow for analysing reviews is described in detail, from data collection using the YouTube Data API and web scraping methods, to tokenisation and lemmatisation, and the application of predictive analytics to recognise human emotions within the framework of "affective computing". The scientific novelty lies in justifying a specialised research infrastructure for strategic management in tourism that integrates corrective nodes in the form of expert analysts who verify the results of machine algorithms, thereby enhancing the reliability of market monitoring and identification of hybrid threats in the information space. The research's practical significance lies in its ability to transform qualitative consumer preferences into quantitative arguments for the strategic planning of recreational infrastructure and the rebranding of the national tourism product, based on information support from public and private institutions. The proposed approach, which is based on the use of open-source libraries that are readily available, enables small and medium-sized enterprises to implement intelligent market analysis systems that go beyond the scope of traditional marketing. These systems allow for the proactive management of tourist flows amidst the transformation of the global economy. At the state level, they enable the prevention of the impact of hybrid threats.

Author Biography

N.A. Dekhtyar , Simon Kuznets Kharkiv National University of Economics, Department of International Economics and Management, Nauky Ave., 9A, Kharkiv, 61165, Ukraine

PhD (Econ.), Associate Professor     

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Published

2026-08-26

How to Cite

Dekhtyar , N. (2026). USING AI MODELS IN THE CONTENT ANALYSIS OF POTENTIAL TOURISM DEMAND. The Actual Problems of Regional Economy Development, 2(22), 72–88. https://doi.org/10.15330/apred.2.22.72-88

Issue

Section

The development of tourism, hotel and restaurant business