Tourist reviews provide essential insights into travellers experiences and public perceptions of destinations. In Kazakhstan, however, sentiment analysis, particularly using ensemble learning, remains underexplored for evaluating such reviews. This study proposes a hybrid stacking ensemble for sentiment analysis of English-language tourist reviews about Kazakhstan, integrating four complementary approaches: VADER, TextBlob, Stanza, and Local Context Focus Mechanism with Bidirectional Encoder Representations from Transformers (LCF-BERT). Each model contributes distinct analytical capabilities, including lexicon-based polarity detection, rule-based subjectivity evaluation, generalised star-rating estimation, and contextual aspect-oriented sentiment classification. The evaluation utilised a cleaned dataset of 11,454 TripAdvisor reviews collected between February 2022 and June 2025. The ensemble aggregates model outputs through majority and weighted voting strategies to enhance robustness. Experimental results (accuracy 0.891, precision 0.838, recall 0.891, and F1-score 0.852) demonstrate that the proposed method KazSATR outperforms individual models in overall classification accuracy and exhibits superior capacity for aspect-level sentiment detection. These findings underscore the potential of the hybrid ensemble as a practical and scalable tool for the tourism sector in Kazakhstan. By leveraging multiple analytical paradigms, the model enables tourism professionals and policymakers to better understand traveller preferences, identify service strengths and weaknesses, and inform strategic decision-making. The proposed approach contributes to advancing sentiment analysis applications in tourism research, particularly in underrepresented geographic contexts.

The paper presents a model of information system designed to evaluate tourism objects based on information from web resources and social networks. When describing the model, the main entities such as data object, user, option, comment, tourism object, tourism organization and tone definition term were identified and described in detail. The relationships between the above entities of the information system are also defined. The core of the model is a data object, which stores complete information about the resource and is described to a greater extent through metadata. The main stages of model realization are given, which includes four steps: creation of tourism object database, collection and processing of comments, creation of tone definition dictionary and stage of tourism object evaluation. The paper also touches upon the issues related to the definition of the needs of tourists and tourism organizations.

The digital transformation of tourism has turned traveler reviews into a continuously expanding record of what visitors value, yet this evidence is fragmented across platforms and languages and is usually reduced to star ratings that obscure the underlying structure of preferences. This study develops a method for the aspect-based semantic analysis of tourist preferences and applies it to Kazakhstan, an emerging destination whose digital space spans three languages. We assembled a corpus of 571,153 reviews from 2GIS, Booking, TripAdvisor, and GetYourGuide in Russian, Kazakh, and English, balanced it to 234,663 reviews, and annotated a gold set of 1500 reviews against an 11-aspect taxonomy. Aspect categories were detected with a linear support vector model, and their sentiment was classified with a multilingual transformer, enabling reliable processing of short, code-mixed texts. The results show that platforms do not merely differ in tone but describe fundamentally different destinations. 2GIS, which carries the domestic voice, diverges sharply from the international platforms, whereas TripAdvisor and GetYourGuide are almost indistinguishable. Domestic and inbound tourists likewise emphasize different aspects, with food and service dominating domestic reviews and location, infrastructure, and nature dominating inbound ones. These findings demonstrate that any single-source analysis yields a systematically biased picture of a destination and argue for multilingual, multi-platform designs in tourism analytics.

The classical working set (WS) model limits thrashing by keeping in memory all pages referenced within a fixed window delta. However, a constant delta cannot adapt to changing locality across execution phases, leading to either inefficient memory utilization or increased page faults. This paper treats delta as a controlled variable rather than a configuration constant. We define a feedback controller that samples the fault rate over fixed reference epochs and enlarges or reduces delta by a bounded step when the rate crosses an upper or a lower threshold. A dead band between the thresholds, a bounded adjustment step and a cooldown interval together bound the adaptation rate and exclude sustained oscillation. The controller needs no offline profiling and no per-workload parameter search, and it leaves the eviction rule itself unchanged, so setting the step to zero recovers the classical policy exactly and any measured difference isolates the effect of adaptation alone. We evaluate the policy in a trace-driven simulator against least recently used (LRU) and first-in, first-out (FIFO), not recently used (NRU), and the static WS policy on phase-changing, cyclic and random-uniform reference streams, reporting fault counts with confidence intervals. We also state the regimes in which adaptation is expected to give no benefit.

Poor code locality in virtual memory systems is one of the reasons for page faults and, consequently, slow operation of an entire system. Despite the extensive body of research dedicated to minimizing page faults, the proposed solutions, which are predominantly based on clustering techniques, fail to provide approximation errors relative to an unknown optimal or near-optimal solution. We use Working Set strategy and geometric interpretation of the computational process, which clarifies the subtleties of optimization and facilitates the development of a mathematical model for minimization of page faults. Our approach includes functionals and constraints that define a set of possible solutions, which may be useful for future research aimed at developing an algorithm to achieve an optimal or ε-optimal solution. The results pave the way for researching and finding an efficient and cost-effective replacement algorithm similar to the working set approach.

Poor code locality in virtual memory systems significantly contributes to page faults, leading to degraded system performance. Although many solutions aim to minimize page faults, most rely on clustering techniques that do not quantify the approximation error relative to the optimal solution. In this work, we develop a novel mathematical model based on the Working Set strategy combined with a geometric interpretation of the computational process via a Hasse diagram. This approach enables the reduction of the problem dimensionality and facilitates identification of critical control states under realistic constraints. We formalize the minimization of expected page faults as a discrete optimization problem with well-defined functionals and constraints. Experimental evaluation demonstrates that our model achieves lower average page faults and execution times compared to classical algorithms, especially under poor code locality conditions. Our method also provides a foundation for obtaining ε-optimal solutions and paves the way for designing efficient and cost-effective page replacement algorithms with provable guarantees. These contributions establish both theoretical and practical advances in virtual memory management.