징진지 공동 발전 전략 동향 연구 - 인민일보 기사의 토픽 모델링 분석(2014-2024)

People sourcing for a particular job role in any corporate venture is a painstaking, progress-impeding task, especially given the changing job market trends, quality of candidate profile and the sheer number of applicants. This paper elucidates an improvised topic modelling approach, where the semantic analysis stage is prepended with text summarization, for the procedure of people sourcing for job roles using topic modelling and machine learning techniques. The distinction between choosing the perfect candidate for the job and choosing a good candidate who is adept in certain domains but not relevant for the job and getting the candidate up to speed by providing on job training, and the downtime involved in the process is certainly a deciding factor in hiring a potential employee. The following paper aims to alleviate this issue by describing the algorithm which identifies the most suitable candidate in the applicants’ pool through a novel, robust approach which uses topic modelling techniques like Latent Semantic Indexing (LSA) and Latent Dirichlet Allocation (LDA). It is empirically found that the precision of profile matching task was enhanced by using text summarization (through TextRank model) for both LSA and LDA by 21.4% and 50% respectively, and LSA outperforming LDA with regard to precision, in both with and without text summarization cases by 23.8% and 51.57%, respectively.

With the remarkable development of China’s economy, the growing of tourism, ethnic sports gradually rise, more and more people into ethnic areas, explore ethnic customs, participate in ethnic sports. Based on the new economy, new infrastructure, new customer groups and new needs, to launch innovative and diversified development challenges for national sports tourism. By using the research methods of literature, logic analysis and mathematical statistics, this paper discusses the development obstacles and development paths of the integration of sports intangible cultural heritage and cultural tourism in Sichuan ethnic areas. The results show that the top-level design of sports intangible cultural heritage and sports tourism industry in minority areas is not mature; The development of national sports event tourism industry is unbalanced; The integration and development of sports intangible heritage and cultural tourism in minority areas are unbalanced. It is concluded that further integrated development requires scientific development planning; Digging the traditional national sports, creating a new model of integrated sports tourism; Improve the two-way national sports tourism development model at home and abroad. The formation of the integrated development model of “intangible cultural heritage + sports tourism” in Sichuan will help the sports tourism industry in Sichuan gradually mature and enhance the soft power of national sports and sports tourism.

In an era dominated by an unprecedented deluge of textual information, the need for effective methods to make sense of large datasets is more pressing than ever. This article takes a pragmatic approach to unraveling the intricacies of topic modeling, with a specific focus on the widely used Latent Dirichlet Allocation (LDA) algorithm. The initial segment of the article lays the groundwork by exploring the practical relevance of topic modeling in real-world scenarios. It addresses the everyday challenges faced by researchers and professionals dealing with vast amounts of unstructured text, emphasizing the potential of topic modeling to distill meaningful insights from seemingly chaotic data. Moving beyond theoretical abstraction, the article then delves into the mechanics of Latent Dirichlet Allocation. Developed in 2003 by Blei, Ng, and Jordan, LDA provides a probabilistic framework to identify latent topics within documents. The article takes a step-by-step approach to demystify LDA, offering a practical understanding of its components and the Bayesian principles governing its operation. A significant portion of the article is dedicated to the practical implementation of LDA. It provides insights into preprocessing steps, parameter tuning, and model evaluation, offering readers a hands-on guide to applying LDA in their own projects. Real-world examples and case studies showcase how LDA can be a valuable tool for tasks such as document clustering, topic summarization, and sentiment analysis. However, the journey through LDA is not without challenges, and the article candidly addresses these hurdles. Topics such as determining the optimal number of topics, the sensitivity of results to parameter settings, and the interpretability of outcomes are discussed. This realistic appraisal adds depth to the article, helping readers navigate the nuances and potential pitfalls of employing LDA in practice. Beyond the technical intricacies, the article explores the broad spectrum of applications where LDA has proven its efficacy. From text mining and information retrieval to social network analysis and healthcare informatics, LDA has left an indelible mark on diverse domains. Through practical examples, the article illustrates how LDA can be adapted to different contexts, showcasing its versatility as a tool for uncovering latent patterns. Keywords: Topic Modeling, Latent Dirichlet Allocation, Text Mining, Natural Language Processing, Document Clustering, Bayesian Inference.

The holy Quran is one of the Holy Books of God. It is considered one of the main references for an estimated 1.6 billion of Muslims around the world. The Holy Quran language is Arabic. Specialized as well as non-specialized people in religion need to search and lookup certain information from the Holy Quran. Most research projects concentrate on the translation of the holy Quran in different languages. Nevertheless, few research projects pay attention to original text of the holy Quran in Arabic language. Keyword search is one of the Information Retrieval (IR) methods but will retrieve what is called exact search. Semantic search aims at finding deeper meanings of a text, and it is a hot field of study in Natural Language Processing (NLP). In this paper topic modeling techniques are explored to setup a framework for semantic search in the holy Quran. As the Holy Quran is the word of God, its meanings are unlimited. In this paper the words of chapter Joseph (Peace Be Upon Him (PBUH)) from the Holy Quran is analyzed based on topic modeling techniques as a case study. Latent Dirichlet Allocation (LDA) topic modeling technique has been applied in this paper into two structures (Hizb Quarters and verses) of Joseph chapter as: words, roots and stems. The log-Likelihood has been calculated for the two structures of the chapter. Results show that the best structure to use is verses, which gives the least energy for data. Some of the results of the attained topics are shown. These results suggest that topic modeling techniques failed to capture in an accurate manner the coherent topics of the chapter.

Latent Dirichlet allocation (LDA) is a statistical model that is often used to discover topics or themes in a large collection of documents. In the LDA model, topics are modeled as discrete distributions over a finite vocabulary of words. The LDA is also a popular choice to model other datasets spanning a discrete domain, such as population genetics and social networks. However, in order to model data spanning a continuous domain with the LDA, discrete approximations of the data need to be made. These discrete approximations to continuous data can lead to loss of information and may not represent the true structure of the underlying data. We present an augmented version of the LDA topic model, where topics are represented using Gaussian mixture models (GMMs), which are multi-modal distributions spanning a continuous domain. This augmentation of the LDA topic model with Gaussian mixture topics is denoted by the GMM-LDA model. We use Gibbs sampling to infer model parameters. We demonstrate the utility of the GMM-LDA model by applying it to the problem of clustering sleep states in electroencephalography (EEG) data. Results are presented demonstrating superior clustering performance with our GMM-LDA algorithm compared to the standard LDA and other clustering algorithms.

Studied the development for the Traditional National leisure-Sports Tourism of Qiang Nationality based on Symbiosis Theory. Investigated the distribution, characteristics and classification of sports tourism resources of Qiang area, analyzed the current situation; status of national leisure sports tourism industry. Put forward specific measures for reconstruction and development of national sports tourism market principles, in order to provide practical basis and theory for leisure-national -sports tourism industry.

Introduction. Antivaccine, denialist, and conspiracy ideas are generally part of an ecosystem of beliefs reinforcing each other. With the advent of covid-19, these ideas figure among the main disinformation contents of counter-official discourses. The article aims to characterize the contents of the Facebook page ‘No to the New World Order’. Method. The content analysis method was employed for the 625 publications on the page during the period studied (October 2020 to January 2022). Analysis. Descriptive statistics were used for the analysis, the topic modeling technique, and the Latent Dirichlet Allocation algorithm. Results. Covid-19 was the main focus of the analysed posts (68%). Many posts criticised government measures, while others alleged censorship and media manipulation of unofficial covid-19 information. Additionally, the posts asserted that the pandemic is part of a New World Order promoted by elites. Conservative ideas were also found, alongside the criticism of health, economic, and financial organisations. The most liked posts included two videos: one about a strike by health workers in France and another criticising vaccines. Conclusions. Based on the published content, it can be observed that 'No to the New World Order' serves as a platform for criticizing pandemic management across all fronts. However, this space does not accommodate divergent opinions, criteria, or perspectives, nor does it offer criticism or questioning of the accuracy of shared content. These conditions foster misinformation and polarization on the issue.

Mining user reviews to discover what the user likes and dislikes is vital to understanding user behaviors. Topic modeling techniques have been extensively used to discover meaningful topics for user reviews and to discover user behaviors. Extracted topics may be a mixture of different concepts and hence very likely to be less coherent and unclear, especially when extracting a relatively small number of topics. As such, we propose a method that extracts a relatively large number of topics using a topic modeling technique and relies on hierarchical clustering to exploit semantic distances between topics, to generate a small number of highly coherent and clear topics. We also compare this set of topics representing hidden user concerns extracted by our approach with those derived using LDA (Latent Dirichlet Allocation) and a hierarchical variant called Pachinko Allocation Model (PAM) and show that our method generates more coherent user concerns. Further, we also demonstrate how a hierarchical model of user concerns can be automatically generated by exploiting our approach. Such a hierarchy may help capture the conceptual distances between various user concerns and inherent similarities between users having those concerns.

Abstract. Problem. The rapid advancement of artificial intelligence (AI) has significantly impacted various domains, particularly education. AI-driven tools are increasingly being integrated into teaching methodologies, assessment frameworks, and curriculum design. However, despite the growing research interest in Artificial Intelligence in Education (AIED), the field remains fragmented, lacking a comprehensive analysis of dominant research themes and emerging trends. Understanding these trends is crucial for optimizing AI applications in education and shaping future developments in the field. Goal. The primary goal of this study is to analyze the current landscape of AIED research by identifying key thematic clusters, evaluating dominant research directions, and predicting future trajectories in AI-driven education. Methodology. This study employs network analysis, topic modeling, and global research trend evaluation to analyze scientific publications in AIED. The Latent Dirichlet Allocation (LDA) algorithm is applied to classify research articles into thematic categories, enabling the identification of primary topics shaping the field. Data is sourced from the Web of Science database, and hierarchical clustering techniques are used to determine semantic similarities between thematic models. Results. The analysis reveals five major research clusters in AIED, including AI applications in medical education, machine learning in education, adaptive learning technologies, large language models in learning, and student-centered AI-driven educational strategies. The study identifies increasing trends in the adoption of generative AI and chatbot-based learning support, while traditional AI-driven assessment methodologies show moderate growth. The results also highlight strong interconnections between AI-driven personalization and intelligent tutoring systems, emphasizing the shift toward adaptive and student-centric learning environments. Originality. This research provides a structured and systematic analysis of AIED trends, leveraging advanced topic modeling techniques. Unlike previous studies focusing on isolated AI applications in education, this study presents a holistic view of thematic structures in AIED research, offering a quantitative and data-driven approach to understanding the evolution of the field. Practical Value. The findings can serve as a strategic reference for educators, policymakers, and AI developers in shaping future educational technologies. By identifying key thematic directions, this research supports evidence-based decision-making for integrating AI into educational systems. The results can also guide further research in AIED, helping scholars explore emerging AI-driven teaching methodologies, adaptive learning models, and ethical considerations in AI-powered education.

BackgroundBioinformatics is an interdisciplinary field at the intersection of molecular biology and computing technology. To characterize the field as convergent domain, researchers have used bibliometrics, augmented with text-mining techniques for content analysis. In previous studies, Latent Dirichlet Allocation (LDA) was the most representative topic modeling technique for identifying topic structure of subject areas. However, as opposed to revealing the topic structure in relation to metadata such as authors, publication date, and journals, LDA only displays the simple topic structure.MethodsIn this paper, we adopt the Tang et al.’s Author-Conference-Topic (ACT) model to study the field of bioinformatics from the perspective of keyphrases, authors, and journals. The ACT model is capable of incorporating the paper, author, and conference into the topic distribution simultaneously. To obtain more meaningful results, we use journals and keyphrases instead of conferences and bag-of-words.. For analysis, we use PubMed to collected forty-six bioinformatics journals from the MEDLINE database. We conducted time series topic analysis over four periods from 1996 to 2015 to further examine the interdisciplinary nature of bioinformatics.ResultsWe analyze the ACT Model results in each period. Additionally, for further integrated analysis, we conduct a time series analysis among the top-ranked keyphrases, journals, and authors according to their frequency. We also examine the patterns in the top journals by simultaneously identifying the topical probability in each period, as well as the top authors and keyphrases. The results indicate that in recent years diversified topics have become more prevalent and convergent topics have become more clearly represented.ConclusionThe results of our analysis implies that overtime the field of bioinformatics becomes more interdisciplinary where there is a steady increase in peripheral fields such as conceptual, mathematical, and system biology. These results are confirmed by integrated analysis of topic distribution as well as top ranked keyphrases, authors, and journals.

Stack Overflow has undoubtedly become a big trend among the programmers and researchers. The problems or doubts faced by any programmer are put on this forum making it available to many knowledgeable people and hence giving out possibly correct solutions. This paper focuses on Tag based Answer Recommendation System on Stack Overflow. This model follows Topic Modeling methodology wherein two techniques were attempted, i.e., LSA(Latent semantic analysis) and LDA(Latent Dirichlet Allocation). These methods deal with various User Queries to find out the most similar questions from the database and hence, deliver the most valid answers related to the question as output. The results from both the Topic Modeling techniques were analyzed with the help of Cosine Similarity as metrics and hence, we found out that LDA performed better than LSA, thereby giving better and more accurate results.

Text analysis is an important component in the evolution of recommender systems, as it enables meaningful information to be extracted from vast amounts of textual data. This study performs a comparative analysis of two main topic modeling techniques, Latent Dirichlet Allocation (LDA) and BERTopic in the context of news recommender systems. Using a dataset of Moroccan news articles, we evaluate the ability of these models to generate coherent and interpretable topics. Our results demonstrate that BERTopic outperforms LDA in terms of topic consistency and semantic richness, particularly when applied to large and diverse datasets. Moreover, the integration of BERTopic into a content-based recommender system results in improved relevance and accuracy of recommendations. This research highlights the potential of advanced topic modeling techniques for refining recommender systems and suggests future directions, including the exploration of hybrid models and the integration of sentiment analysis for further improvements.

In the context of the new development stage, Hainan Province, relying on its unique natural resources and policy advantages, promotes the deep integration of outdoor sports and sports tourism through diversified forms such as sports events and outdoor adventures, showing great potential in economic, social, and environmental aspects, and gradually becoming an important driving force for the high-quality development of regional economy. However, in the process of development, it also faces many difficulties, including the small scale of the sports industry, the lack of innovative products, the lack of business coordination, and the imperfect policy system. In order to cope with these challenges, approaches such as integrating resources, cultivating characteristic IP projects, optimizing the policy environment, and enhancing the ability of collaborative innovation in multiple formats are proposed to further promote the modernization and sustainable development of Hainan's sports tourism industry, help Hainan occupy an important position in the global pattern of cultural and tourism integration, and explore innovative development paths with global influence.

IntroductionThe spatial distribution of sports tourism projects in China exhibits significant regional disparities, creating challenges associated with efficient resource utilisation and equitable development.MethodsIn this study, we applied kernel density estimation, spatial autocorrelation analysis, and the geographic detector to examine the spatial patterns and driving factors of STPs across the country systematically.ResultsThe results reveal an uneven distribution of STPs, with the Yangtze River Delta and Beijing-Tianjin-Hebei region identified as the two core areas with the highest density. Natural STPs are more widely distributed, whereas cultural STPs are primarily concentrated in economically developed regions. Factor detection reveals that economic and fiscal variables serve as dominant drivers and that the balance of deposits in financial institutions, general public budget expenditures, and tertiary industry operating income exert the greatest explanatory power, whereas transportation and environmental indicators are less influential.DiscussionThese findings highlight the combined effects of natural endowments, cultural resources, and institutional support on sports tourism development. This study advances the theoretical understanding of STP spatial dynamics and provides practical insights for optimising resource allocation, narrowing regional disparities, and promoting the sustainable growth of China's sports tourism industry.

The lack of long-term, sustainable strategic planning and development for sports and tourism in Kenya is a challenge for Kenya's sports tourism industry. The purpose of the study was thus to determine the impact of risk assessment methodologies on sporting tourism events. The specific objectives of the study were: to create a profile of the sports tourism events and venues, to establish the effect of crowd management and risk assessment strategies on sports tourism events in Nairobi County, Kenya; and to determine the moderating effect of financial capability on the relationship between crowd management, risk assessment strategies, and sports tourism events in Nairobi County, Kenya. In this study, a descriptive research approach was utilized, and the population of interest consisted of 17 prominent sporting event venues located within Nairobi County. The research focused its attention on a total of 340 participants in addition to 69 managers. The participants in the study, which consisted of 17 managers and 105 sportspeople, were chosen using a technique called purposeful sampling. In order to obtain data that reflect the ideas and opinions of the respondents in an unfiltered manner, questionnaires and an interview guide were used. A linear regression model was used to link the independent variable to the dependent variable. Result outputs were represented in tables, charts, and bars. The multiple linear regression analysis findings revealed a coefficient of determination of 0.700, implying that the independent variable used in this study; crowd management and risk management strategies, explains 70% of the variation in sports tourism events in Nairobi County, Kenya. The study established that there was a positive and statistically significant relationship between Crowd management and risk management strategies and the success of sports tourism events in Nairobi County (β=0.837, p=0.000<0.05). The study also established that financial capability had moderating effect on the relationship between crowd management and risk assessment strategies and the success of sports tourism events in Nairobi County. The study thus recommends a multi-agency approach to strengthen risk assessment and crowd management protocols. Keywords: Crowd Management, Sports Tourism Events, Risk Assessment Strategies, Kenya Sports Tourism Safety, Nairobi County Sports Events