Topic Modeling Approach Research Articles

Mapping the Evolving Landscape of Library-Related Policies in China: A Topic Modeling Approach

This study aims to investigate the thematic characteristics and evolutionary patterns of library policies in China from 1949 to 2023. Employing the Latent Dirichlet Allocation (LDA) model, the research analyzed 2,703 Chinese government-issued policies related to libraries during this period, identifying key topics and tracing their developmental trajectories. The study recognized four distinct developmental phases, five major topics, and the underlying connections among feature terms within different topics. It discussed the corresponding topic strengths and evolving trends in each phase. Library policies in China are predominantly centered on five key domains: 1) spatial planning and architectural integration, 2) information management and transparency, 3) legal regulations and supervisory enforcement, 4) heritage preservation and sustainable development, and 5) community inclusion and accessibility. Notably, spatial planning and architectural integration consistently garner substantial attention, highlighting their enduring prominence, while there is relatively less emphasis on community inclusion and accessibility, as well as heritage preservation and sustainable development. The contribution of this study lies in conducting a semantic exploration of the overall transformation in library-related policies in China, validating the effectiveness of the LDA model in identifying topics within policy texts, and expanding the research perspective on library policy evolution.

A Topic Modeling Approach Towards Understanding the Discourse between Religion and Videogames on Reddit

Today's videogames are often intertwined with profound religious narratives, content, characters, and artifacts - but how do player communities understand the role of religion in games, and what is the impression of believers on the use, portrayal, and representation of the religion they practice? This paper dives deep into this confluence by rigorously analyzing discussions from religion and videogame subreddits that often depict the most popular communication channel for their communities. Following topic modeling, we cluster posts and comments from the three major religious groups and the most popular related videogame subreddits, bringing forth representative cultural sentiments and patterns of players and religious stakeholders. Through this, we discovered 22 distinct sub-themes, further categorized into the three overarching themes of (a) Blasphemous Elements of Videogames, (b) Religion as a Design Space for Videogames, and (c) Videogames for Religious Education and Community Forming.

Exploring Research Trends on Digital Health in Nursing Science in Korea: A Topic Modeling Approach.

Digital health technology is utilized in contemporary nursing practice and education. This review explored the scope of digital health applications and major trends in nursing research involving digital health in Korea using topic modeling. Our analysis of data using the Latent Dirichlet Allocation model identified four distinct research topics: nursing education using digital technologies (35.17%), hospital-based nursing practice using digital technologies (19.88%), digital technologies for health education (25.75%), and development of digital technologies to support self-management of chronic conditions (19.20%). Our findings reveal trends, current issues, and gaps in digital health nursing research.

The exploration of users’ perceived value from personalization and virtual conversational agents to enable a smart home assemblage– A mixed method approach

Advancements in smart home technologies are beneficial and valuable for both users and firms. Nevertheless, users’ acceptance of these applications are considerably influenced by the need for user personalization, complex and dynamic within a smart home environment, and thus, can present limitations. Additionally, the role of a virtual conversational agent, both as an individual entity and as part of a collective network, in the setup and management of various smart home devices warrants further exploration. To investigate the enablers and inhibitors of smart home adoption and the role of virtual conversational agents, a mixed-method design was employed, comprising two studies: a survey of 1403 users (Study 1) and a topic modeling approach based on 355,340 Reddit posts (Study 2). The findings indicate that the usefulness of personalized applications and services, as well as the integration of a virtual conversational agent as a central component, are crucial in facilitating smart home adoption. The empirical analyses provide significant implications for research and practice by extending existing technology acceptance and use models to include smart device assemblages.

Exploring the Educational Landscape of ChatGPT: A Topic Modeling Approach on Twitter Data

In the rapidly evolving landscape of Artificial Intelligence (AI), platforms like ChatGPT are reshaping the educational domain, prompting deeper explorations into the nature and depth of this intersection. This study aimed to systematically uncover the prevailing sentiments, concerns and discussions on Twitter surrounding ChatGPT’s role in education. Through an extensive data collection process, over 3.8 million tweets were initially gathered, followed by rigorous refining processes that included expert-driven tweet labelling and subsequent classification using Machine Learning (ML) and deep learning models. The cleaned dataset underwent a series of preprocessing steps and feature extraction and was ultimately subjected to Latent Dirichlet Allocation (LDA) for topic modelling. Our findings unveiled 15 distinct topics that broadly spanned common discussions, AI implementation, and its potential impacts. The data’s visualisation using t-distributed stochastic neighbour embedding (t-SNE) showcased a dense central clustering of these topics. In conclusion, our research underscores the multi-faceted dialogues on AI, particularly ChatGPT, in education, emphasising the pressing need for continued discourse and research as AI tools further integrate into our educational paradigms.

Tracing the technological trajectories of smart logistics domain based on citation analysis and topic modelling

ABSTRACT The emergence and development of intelligent technology are triggering major changes in the logistics industry, and relevant scientific and technological research on smart logistics (SL) is emerging endlessly. However, there is no research that systematically explores SL from the technological perspective. Therefore, this article selects 47,253 documents retrieved from the Derwent Innovation Index for systematic analysis and provides a comprehensive understanding of the SL domain. We combine main path analysis (MPA) and LDA topic modelling approaches to identify technological evolution trajectories. Through three main paths, the key technologies and technological evolution directions in the SL domain are revealed. Through topic identification and topic correlation calculation, global topic popularity trends and local topic evolution paths are identified. Finally, this study summarises our findings, contributions, and limitations. This is the first attempt to detect key technologies in the SL domain through MPA and topic analysis, thereby guiding relevant researchers and enterprises.

Exploring research topics and trends in early childhood education using structural topic modeling

Early childhood education plays a key role in fostering child development and preparing children for school and life. The aim of the present paper is to give an overview of research topics in early childhood education research based on the analysis of 39,926 scientific articles published between 2000 and 2021 and to explore research trends over time. Therefore, a structural topic modelling approach was used. The analyses show a strong increase in publication activity over the last years as well as the diversity of research topics, which provide important insights for achieving the UN Sustainable Development Goals in various areas (cultural diversity, design of inclusive learning environments, educational institutions, professional development issues, educational policy and reforms, etc.). Beside topics like cultural diversity, and inclusive learning environments more specific research topics, for example self-regulation, executive functions, numeracy, language development and physical activity have been increasingly addressed in publications over the years.

Unpacking the relation between media sentiment and house prices: A topic modeling approach

This study uses a topic modeling approach to investigate the relation between news media sentiment and house price movements. By examining real estate related articles published in local newspapers of 16 major cities in Canada and Australia, we find that housing media sentiment has significant relation with future house price movements in the Canadian market but not in the Australian market. We identify the specific topics discussed in news media related to the housing market and report differences in their themes and media sentiment's predictive power between Canada and Australia. This analysis presents novel inferences of qualitative and hard-to-quantify information related to the housing market in two different countries.

Unveiling mobility patterns beyond home/work activities: A topic modeling approach using transit smart card and land-use data

In this paper, a probabilistic topic modeling algorithm called Latent Dirichlet Allocation (LDA) is implemented to infer trip purposes from activity attributes revealed from smart card transit data in an unsupervised manner. While most literature focused on finding patterns for home and work activities, we further investigated non-home and non-work-related activities to detect patterns associated with them. Temporal attributes of activities are extracted from trip information recorded by Tehran subway’s automatic fare collection system. In addition, land-use data is also incorporated to further enhance spatial attributes for non-home/work activities. Various activity attributes such as start time, duration, and frequency in addition to land-use data are used to infer the activity purposes and patterns. We identified 14 different patterns related to non-commuting activities on the basis of both their temporal and spatial attributes including educational, recreational, commercial, and health and other service-related activity types. We investigated passengers’ activity pattern and behavior changes before and during COVID-19 pandemic by comparing the discovered patterns. For recreational patterns it is revealed that not only has the number of recreational patterns dropped, but also the duration of recreational activities decreased. Morning patterns of educational activities have also been eliminated and number of commercial activities was decreased during COVID-19. The proposed model demonstrates the ability to capture travel behavior changes for different disruptions using smart card transit data without performing costly and time consuming manual surveys which can be useful for authorties and decision makers.

Mapping Russian International Relations: A Topic Modelling Approach

Mapping Russian International Relations: A Topic Modelling Approach

Ai Art In Mobile: Topic Modeling Of User Reviews For Generative Ai Applications

The generative AI concept, which enables users to create text, image, and video content through prompts, is revolutionizing the content side and AI applications in marketing. Despite the increasing popularity of generative AI applications, the market perception regarding generative AI remains underexplored. This study aims to explore the generative AI market perception through the context of mobile applications with the help of user reviews. The study follows a structured approach including identifying the generative AI mobile applications, assessing the context through rating scores and install amounts of mobile applications, and using a topic modeling approach (BerTopic) for online reviews to identify the topics included in the conversation. 8159 user reviews from 22 mobile applications are used as sample of the study and the average rating score for the sample found as 4,06 which signals a positive perception of market. The study concludes top ten topics as; “Dissatisfaction About Amount of Advertisements in app”, “NSFW Content and Moderation”, “Praise of Application”, “Functionality Problems & Crashes”, “Payment Necessity and Trial Problems”, “In-App Purchase Restoration Problems”, “Specific Feature in App”, “Chat Function”, “Credit System” and “Excess of Ads”. The study reveals the main issues of Ai Art mobile applications for marketing decision-making processes.

A framework of attribute extraction and dependable aspect term selection from reviews of hospital websites

<p><span lang="EN-US">Online reviews found on hospital websites and external platforms constitute user-generated content where patients and their families share their firsthand encounters. As patients increasingly rely on online platforms to share their experiences, understanding the importance of their feedback is paramount for healthcare providers. The novelty of this research lies in the development of advanced frameworks that not only extract relevant information but also offer a more sophisticated and coherent analysis of the multifaceted aspects embedded in patient reviews. Hence, this work involves collecting data from various hospital websites, followed by data pre-processing to ensure accuracy and consistency. Subsequently, two distinct frameworks are proposed. The first framework aims to extract specific attributes (topics) mentioned in reviews, enhancing the granularity of information derived from the collected data. The second framework addresses the efficient extraction of aspect terms from pre-processed data, utilizing a coherence score-based approach called as modified latent dirichlet allocation term frequency-inverse document frequency (M-LDA TF-IDF). The M-LDA TF-IDF has achieved better a coherence score of 0.478 which is much better in comparison with other topic modelling approaches.</span></p>

Examination of experience attributes of parks in urban tourist destinations and their influence on visitor satisfaction: a topic modelling approach

ABSTRACT Understanding visitors’ park experience in urban tourist destinations to enhance their satisfaction is crucial for effective park management and urban planning. Through a topic modelling analysis using user-generated data from TripAdvisor and employing the Latent Dirichlet Allocation (LDA) algorithm, 20 attributes of urban park experience are identified and grouped into three categories and five dimensions. Besides confirming some of the attributes of park experience identified in previous research, this study also uncovers new attributes that provide insights into visitors’ urban park experience related to the historical and cultural aspects (e.g. filming location, carriage rides and rickshaws). Furthermore, positive and negative effects of each attribute on visitor satisfaction are identified. Findings also suggest significant seasonal difference in attributes of visitors’ urban park experience.

Understanding Narratives of Uncertainty in Fertility Intentions of Dutch Women: A Neural Topic Modeling Approach

Understanding Narratives of Uncertainty in Fertility Intentions of Dutch Women: A Neural Topic Modeling Approach

Applying Large Language Models to Interpret Qualitative Interviews in Healthcare.

To address the persistent challenges in healthcare, it is crucial to incorporate firsthand experiences and perspectives from stakeholders such as patients and healthcare professionals. However, the current process of collecting, analyzing and interpreting qualitative data, such as interviews, is slow and labor-intensive. To expedite this process and enhance efficiency, automated approaches aim to extract meaningful themes and accelerate interpretation, but current approaches such as topic modeling reduce the richness of the raw data. Here, we evaluate whether Large Language Models can be used to support the semi-automated interpretation of qualitative interview data. We compare a novel approach based on LLMs to topic modeling approaches and to manually identified themes across two different qualitative interview datasets. This exploratory study finds that LLMs have the potential to support incorporating human perspectives more widely in the advancement of sustainable healthcare systems.

Topic Clusters of Successful Aging Studies: Results of a Topic Modeling Approach.

Literature regarding successful aging reflects a wide variety of fields and perspectives. Given the range of definitions and approaches found in published literature, it is important to investigate clusters of topics studied over time. This study aimed to show the change of topic clusters within successful aging studies. The study used topic modeling methodology to analyze vast amounts of abstract data. Among publications collected from Scopus (4,458) and Web of Science (5,187), 5,610 publications were analyzed. Topic clusters were analyzed in two ways: by a) division of time (1960s - 1990s, 2000s, 2010s, 2020s) and b) all years combined. In the 1960s - 1990s, 11 topic clusters ranging from health to emotional well-being emerged without any dominant domain. In the 2000s, two clusters related to social support and health appeared as major clusters. In the 2010s, one topic cluster that included words related to health and social participation was the biggest. In the 2020s, emotional health and social participation appeared again as one of the major clusters and health-related topics started to diverge into subgroups like physical health and mental health. In all years of publications combined, the major cluster involved words that are related to either health or social domains. Results revealed that successful aging has been studied in many fields using multidimensional perspectives. The dominant categories were health and social domains. These findings suggest interprofessional practice, an interdisciplinary approach in research, and multi-sector involvement in policy.

Developing an Integrated Topic Modeling Approach for Text-Based Knowledge Accumulation

Developing an Integrated Topic Modeling Approach for Text-Based Knowledge Accumulation

Information Security Research in the Information Systems Discipline: A Thematic Review and Future Research Directions

Information security continues to grow in importance in all aspects of society and therefore evolves as a prevalent research area. The information systems (IS) discipline offers a unique perspective to move this stream of literature forward. Using a semiautomated thematic analysis approach based on the topic modeling technique, we review a broad range of information security literature to investigate how we may integrate and advance our understanding of information security. Our analysis reveals four major themes, including information security policy (ISP) compliance, motivations and susceptibility, software security decisions, and firm security strategy. We also identify a theme of security in broader contexts, in which studies consider the societal impacts of information security and online hacker behavior. This review contributes to IS literature by 1) synthesizing the broad range of information security research published in the top journals of the field, moving beyond prior reviews focusing on a narrower scope such as individual ISP compliance; 2) identifying major themes and proposing an overarching research framework encompassing these themes and their interconnections, allowing us to envision future research directions; and 3) enumerating a semiautomated topic modeling approach that other researchers can employ.

Exploring the technological leaders using tire industry patents: A topic modeling approach

Researchers have long been interested in the transportation industry, recognizing its significant impact on human societies. Specifically, the automobile industry and its components have garnered special attention due to the evolving nature of transportation technologies. With this in mind, this study aims to explore the current and future technologies within the tire industry as a technological-based component of the automotive industry. Additionally, the research examines the key topics, leaders of technology, and strategies of technology-owning companies in these topics. To carry out this investigation, we employed a combination of methods including patent analysis and topic modeling. The selection of leading technology companies was based not only on the number of patents but also on the impact and quality of their patents, which was carefully evaluated. By leveraging these methods and results, tire companies and other industries can gain a competitive edge in technology, acquire insights into the current and future market share of leading companies, and develop effective roadmaps for their own advancement.

Philosophical research in Brazil: A structural topic modeling approach with a focus on temporal and gender trends

AbstractThis paper employs structural topic modeling (STM) to describe the academic philosophy landscape in Brazil. Based on a public national database, a corpus consisting of 12,515 abstracts of monographs defended in philosophy graduate programs between 1991 and 2021 was compiled. The final STM model identified 74 meaningful research topics, clustered into 7 thematic categories. This study discusses the prevalence of the most significant topics and categories, their trends across three decades, and their (positive or negative) association with the supervisor's gender. Results show the first empirical evidence that Brazilian philosophical research exhibits a greater focus on philosophers than on specific themes or problems. Moreover, by visualizing the variations in topic prevalence over time, it was possible to track the rise or decline of the major interest categories and topics. Finally, results also show which topics are more influenced or less influenced by gender.