Mate Kovacs

The application of topic modeling in the tourism domain has become a popular research topic in the last decade. This study aims to provide a comprehensive bibliometric analysis of topic modeling research in tourism over the period 2010 to 2023. The data for this study were sourced from the Scopus database, a widely recognized repository of peer-reviewed literature. The search was restricted to publications published from January 1, 2010, to December 31, 2023, to capture the evolution and current state of this rapidly growing field. Using VOSviewer and SciMAT software to analyze articles in the Scopus database, the study identified key trends, influential authors, and future research directions. This study indicates the growth and development in topic modeling for tourism research, with more than 100 Scopus-indexed papers published annually in 2023 alone. The results of this study show that topic modeling has a wide range of applications in tourism, demonstrating its utility in various contexts to understand tourist behavior and enhance smart tourism initiatives.

Online petition systems facilitate citizen engagement with government agencies by providing a platform for direct expression of policy concerns. The increasing volume and diverse topical range of submissions, however, present significant challenges in routing petitions to the relevant departments in public administration. This study presents an automated approach for topic categorization of online petitions in South Korea utilizing a BERT-based framework. The methodology integrates data from both legacy (Blue House) as well as current (National Assembly) online petition platforms and fine-tunes KR-BERT, a Korean-specific BERT variant to capture domain-specific linguistic features. The study employs selective layer freezing to balance computational efficiency with domain adaptation. Experiments conducted demonstrate that models trained on combined historical and recent data outperform those relying exclusively on data from the currently used platform. The bestperforming configuration achieved 76% accuracy and an F1score of 0.75 over the 12 petition categories. Although there are inherent challenges in aligning data from older systems and platforms with newer ones, the findings suggest that incorporating historical data and implementing tailored layer-freezing strategies can considerably enhance categorization outcomes in resource-constrained public-sector environments.

The now-globally recognized concerns of AI’s environmental implications resulted in a growing awareness of the need to reduce AI carbon footprints, as well as to carry out AI processes responsibly and in an environmentally friendly manner. Benchmarking, a critical step when evaluating AI solutions with machine learning models, particularly with language models, has recently become a focal point of research aimed at reducing AI carbon emissions. Contemporary approaches to AI model benchmarking, however, do not enforce (nor do they assume) a model initial selection process. Consequently, modern model benchmarking is no different from a “brute force” testing of all candidate models before the best-performing one could be deployed. Obviously, the latter approach is inefficient and environmentally harmful. To address the carbon footprint challenges associated with language model selection, this study presents an original benchmarking approach with a model initial selection on a proxy evaluative task. The proposed approach, referred to as Language Model-Dataset Fit (LMDFit) benchmarking, is devised to complement the standard model benchmarking process with a procedure that would eliminate underperforming models from computationally extensive and, therefore, environmentally unfriendly tests. The LMDFit approach draws parallels from the organizational personnel selection process, where job candidates are first evaluated by conducting a number of basic skill assessments before they would be hired, thus mitigating the consequences of hiring unfit candidates for the organization. LMDFit benchmarking compares candidate model performances on a target-task small dataset to disqualify less-relevant models from further testing. A semantic similarity assessment of random texts is used as the proxy task for the initial selection, and the approach is explicated in the context of various text classification assignments. Extensive experiments across eight text classification tasks (both single- and multi-class) from diverse domains are conducted with seven popular pre-trained language models (both general-purpose and domain-specific). The results obtained demonstrate the efficiency of the proposed LMDFit approach in terms of the overall benchmarking time as well as estimated emissions (a 37% reduction, on average) in comparison to the conventional benchmarking process.

Achieving the Sustainable Development Goals (SDGs) requires collaboration among various stakeholders, particularly governments and non-state actors (NSAs). This collaboration results in but is also based on a continually growing volume of documents that needs to be analyzed and processed in a systematic way by government officials. Artificial Intelligence and Natural Language Processing (NLP) could, thus, offer valuable support for progressing towards SDG targets, including automating the government budget tagging and classifying NSA requests and initiatives, as well as helping uncover the possibilities for matching these two categories of activities. Many non-English speaking countries, including Indonesia, however, face limited NLP resources, such as, for instance, domain-specific pre-trained language models (PTLMs). This circumstance makes it difficult to automate document processing and improve the efficacy of SDG-related government efforts. The presented study introduces IndoGovBERT, a Bidirectional Encoder Representations from Transformers (BERT)-based PTLM built with domain-specific corpora, leveraging the Indonesian government’s public and internal documents. The model is intended to automate various laborious tasks of SDG document processing by the Indonesian government. Different approaches to PTLM development known from the literature are examined in the context of typical government settings. The most effective, in terms of the resultant model performance, but also most efficient, in terms of the computational resources required, methodology is determined and deployed for the development of the IndoGovBERT model. The developed model is then scrutinized in several text classification and similarity assessment experiments, where it is compared with four Indonesian general-purpose language models, a non-transformer approach of the Multilabel Topic Model (MLTM), as well as with a Multilingual BERT model. Results obtained in all experiments highlight the superior capability of the IndoGovBERT model for Indonesian government SDG document processing. The latter suggests that the proposed PTLM development methodology could be adopted to build high-performance specialized PTLMs for governments around the globe which face SDG document processing and other NLP challenges similar to the ones dealt with in the presented study.