Application Of Topic Models Research Articles

Applying Topic Modeling to Literary Analysis: A Review

Abstract This article examines the integration of topic modeling within literary studies, highlighting its potential to transform conventional literary analysis through computational techniques. It reviews the theoretical underpinnings underlying topic modeling, including prominent algorithms like Latent Dirichlet Allocation, Non-negative Matrix Factorization, and Neural Topic Models, and discuss their utility in dissecting large textual corpora to uncover latent thematic and stylistic patterns. The article subsequently addresses the specific methodological steps for effective implementation, spanning text preprocessing model tuning and result interpretation. We further illustrate the diverse applications of topic modeling in literary studies through thematic analysis, comparative studies, and the extraction of cultural and historical insights. Challenges such as model accuracy, technical limitations, and ethical considerations are critically assessed. The review concludes by envisioning prospective future directions that foresee enhanced integration of topic modeling into literary criticism, facilitated by technological and interdisciplinary advancements.

Mapping the technological landscape of green smart buildings: A patent analytics of key topics, leading companies, and technology gaps

In this study, we investigate the technological landscape of green smart buildings, using patent data from the European Patent Office (EPO) from 2007 onwards. Latent Dirichlet Allocation identified five key technology topics, with the HVAC system (Topic 4) emerging as particularly promising. Technology life cycle analysis revealed a growth phase for leading companies' patents after the Kyoto Protocol's second commitment period, with a predicted shift towards maturity after the Paris Agreement. Self-Organizing Maps and Social Network Analysis further pinpointed eight vacant technology nodes and 39 high-centrality patents within them, predominantly held by Japanese companies. This research provides valuable insights into the current state and future trajectory of green smart building technologies, facilitating informed decision-making for industry stakeholders and contributing to a more sustainable built environment. The novelty lies in the combined application of topic modeling, technology life cycle analysis, and network analysis to comprehensively explore the green smart building patent landscape, offering a unique perspective on its evolution and potential future directions.

Exploring Guilin's Destination Image Via User-Generated Content: A Computational Methods

This study quantify mainland China's destination image through international tourists' user-generated content (UGC), with a specific focus on Guilin, utilizing advanced computational techniques such as Latent Dirichlet Allocation (LDA) for topic modeling and sentiment analysis. It systematically evaluates online travel reviews to distill key themes and emotional sentiments that shape Guilin's image, uncovering a rich tapestry of cognitive perceptions and affective reactions related to its natural scenery, cultural engagements, service standards, and logistical facets. The application of topic modeling and sentiment analysis provides a nuanced understanding of the cognitive and affective dimensions defining Guilin's destination image. While positive sentiments largely highlight the region's aesthetic and experiential allure, negative sentiments reveal critical areas for improvement, such as perceived value and infrastructure, which are vital for enhancing tourist satisfaction and reinforcing Guilin's appeal in the global tourism market. This research applies LDA and sentiment analysis to interpret UGC offer a calculable methodological approach for broader application in destination image studies. By aligning perceptual and emotional insights with destination marketing strategies, the findings offer actionable intelligence to optimize tourist experiences and enhance destination loyalty. This approach not only enriches the academic understanding of destination images but also provides practical frameworks for destination managers to harness the full potential of UGC in shaping and refining the global image of tourism locales.

ВІД АНАЛІЗУ ТЕКСТУ ДО МОДЕЛЮВАННЯ ПРИРОДНОЇ МОВИ: КОМПЛЕКСНЕ ДОСЛІДЖЕННЯ

The article presents a comprehensive analysis of modern techniques and technologies used to analyze the tonality of textual data. Through a series of experiments, basic machine and deep learning algorithms such as sentiment analysis were developed to automatically detect and classify emotional tones in texts. Practical applications of these methods are also explored, ranging from social network monitoring to expert analysis and analysis of user reviews. Next, the article delves into the classification of textual data using modern machine and deep learning methods. This section of the paper reviews several classification models, including the Bayesian most-narrow classifier, support vector methods, and neural networks, highlighting their advantages and limitations. In addition, it emphasizes the importance of implementing text classification in various fields, including social network analysis, news article categorization, and automated document processing. In addition, clustering of similar textual data is considered for further analysis. Various clustering algorithms, such as k-means, hierarchical clustering, and spectral clustering, are compared, with special emphasis on their application to large text corpora. A demonstration of the practical application of text clustering, including data organization, topic search, and style identification in paper works, was also demonstrated. Moving on, the article considers the processing of individual thematic structures in textual data and its further analysis. An in-depth analysis of topic modeling techniques, such as the realized Dirichlet distribution (LDA) model, as well as its capabilities and limitations, is explored. Practical applications of topic modeling are demonstrated in various ways, including text collection analysis, news trend detection, and automatic document categorization. Finally, the article discusses some challenges and future prospects for the development of thematic modeling.

THE APPLICABILITY OF TOPIC MODELING TO IDENTIFY ACTORS OF SOCIO-POLITICAL MOBILIZATION USING LOW-CODE ANALYTICAL PLATFORMS

The article presents a contemporary perspective on the analysis of socio-political processes, grounded in the premise that the application of topic modeling through low-code platforms can substantially enhance the quality of research performed by analysts. This enhancement is particularly significant in identifying the pivotal actors and the evolving dynamics within socio-political processes. The authors argue that topic modeling, a relatively novel approach compared to traditional methods, is capable of uncovering relationships and trends that might otherwise remain obscured. In advocating for this approach, the paper proposes an integrated methodology. This methodology is designed to empower researchers in the social sciences, enabling them to effectively utilize these innovative tools. The objective is to deepen their comprehension of the underlying mechanisms that drive socio-political mobilization. To substantiate their argument, the authors present various case studies. These case studies demonstrate the effectiveness of topic modeling in revealing otherwise hidden connections among various actors. Additionally, they illustrate how topic modeling sheds light on the contributions of these actors to the dynamics of mobilization. This approach represents a significant advancement in the field, offering new insights and a more nuanced understanding of complex socio-political landscapes.

Navigating travel constraints for individuals with mental health conditions

ABSTRACT While previous accessible travel studies focused primarily on physical disabilities, this research examined the perceptions and challenges related to travel experiences among individuals with mental health conditions. Grounded on the leisure constraint model, this study captured the distinct travel constraints and experiences shared by individuals with mental health conditions. Utilising the Reddit API, we collected data from several subreddits that cater to various mental health conditions, including autism, attention-deficit hyperactivity disorder, post-traumatic stress disorder, bipolar disorder, anxiety disorders, eating disorders, and depression. Through the application of topic modelling and topic network analysis, 30 themes and 7 communities emerged, spotlighting the pre- and during-travel constraints of individuals with mental health conditions. The resulting travel constraints were identified, leading to their categorisation as intrapersonal, interpersonal, and structural constraints. This categorisation lays the foundation for developing strategies that can enhance the travel experiences and engagements of individuals with mental health conditions.

Crisis in the UK National Health Service: What does it mean, and what are the consequences?

AbstractThere have been more‐or‐less continual suggestions that the UK National Health Service (NHS) has been suffering from one kind of crisis or another since its creation in 1948. If we are to understand the problems the NHS faces, then we need to empirically investigate what kinds of crises it has faced, if such crises have patterns to them, and whether or not they tend to lead to policy change. This article considers NHS crisis in terms of academic accounts of its history, as well in occurrences of the term ‘NHS crisis’ (and its synonyms) in national newspaper headlines from the 1980s up to 2020 through the application of topic modelling. The combination of these two sources of data leads to the construction of a typology of NHS crises. Having constructed this typology, we can then examine the timing and frequency of NHS crises, and consider the relationship between crises and periods of policy change, as well as to the wider economic and social context in which crises occur through the notion of the ‘NHS spatio‐temporal fix’.

Facebook and social representations of Filipino migrant life in Germany: a reflexive computational approach

Computational approaches have grown in prominence amidst advancements in new media and technologies and ever-increasing amounts of digital data. This article critically examines these automated techniques, especially the analytical affordances and concerns that such methods introduce to the study of online migrant and mobility discourses. The paper further argues for a mixed methodology anchored on social representations theory—a contextually sensitive framework that enables reflexive use of computational approaches, i.e., quantitatively analyze but also explore different layers of cultural and linguistic meanings in online diasporic interactions. With Filipino migrants in Germany as a case study and partner community, the study then demonstrates the combined application of topic modeling and ethnographically inspired qualitative analysis on migrant posts in Facebook. The findings are discussed in the form of a cultural reflection on Filipino values and expectations and an advocacy for mixed methodologies grounded on critical, social, and practice-oriented theories.

A survey of topic models: From a whole-cycle perspective

With the rapid development of information science and social networks, the Internet has accumulated various data containing valuable information and topics. The topic model has become one of the primary semantic modeling and classification methods. It has been widely studied in academia and industry. However, most topic models only focus on long texts and often suffer from semantic sparsity problems. The sparse, short text content and irregular data have brought major challenges to the application of topic models in semantic modeling and topic discovery. To overcome these challenges, researchers have explored topic models and achieved excellent results. However, most of the current topic models are applicable to a specific model task. The majority of current reviews ignore the whole-cycle perspective and framework. It brings great challenges for novices to learn topic models. To deal with the above challenges, we investigate more than a hundred papers on topic models and summarize the research progress on the entire topic model process, including theory, method, datasets, and evaluation indicator. In addition, we also analyzed the statistical data results of the topic model through experiments and introduced its applications in different fields. The paper provides a whole-cycle learning path for novices. It encourages researchers to give more attention to the topic model algorithm and the theory itself without paying extra attention to understanding the relevant datasets, evaluation methods and latest progress.

Improvement of maintenance-based Product-Service System offering through field data: a case study

ABSTRACT Knowledge extraction and reuse are critical topics for manufacturing companies willing to strengthen their Product-Service Systems (PSS) offerings. In manufacturing’s maintenance processes, effectiveness and efficiency depend on the ability to learn from past field interventions. Dealing with unstructured descriptions of maintenance activities has prevented manufacturing companies from analyzing them, causing the loss of useful information. Natural Language Processing (NLP) demonstrated high potential, allowing simplified text knowledge extraction and summarization. Besides, the literature presents only a few applications of topic modeling for maintenance improvement in the manufacturing domain. Using a case study, the paper demonstrates the potentialities of NLP adoption to improve not only the maintenance management and execution but also the asset design and management, impacting the whole PSS. In other words, implications will have effects on the operational (e.g. maintenance execution), managerial (e.g. maintenance management), and business levels (e.g. PSS offering definition) of manufacturing firms.

Measuring Social Value of Information Technology: Application of Topic Modelling and System Dynamics

Measuring Social Value of Information Technology: Application of Topic Modelling and System Dynamics

The Dark Threads That Weave the Web of Shame: A Network Science-Inspired Analysis of Body Shaming on Reddit

Deep within online forums, we often stumble across body shaming. Words like “fat” and “ugly” are tossed around, hurting those they target. But can we peel back the layers of these online communities? In this study, social network analysis is used to shine a light on body shaming on Reddit, a well-known online platform. This paper presents a comprehensive social network analysis of body shaming on Reddit, one of the largest online platforms. The research delves into the intricacies of body shaming by identifying key actors, communities, and patterns of behavior and communication related to body shaming. The results show how behavior and communication differ across Reddit’s various subgroups, and how user activity and the length of comments can vary. Through the application of topic modeling, the main subjects discussed in each subgroup were identified. This enables an understanding of what drives discussions about body shaming. The findings provide valuable insights into the spread and normalization of harmful behaviors and attitudes related to body shaming, which can inform the development of targeted interventions aimed at reducing this harmful behavior and promoting more positive and inclusive attitudes towards body image and weight.

Application of Topic Modeling on Artificial Intelligence Studies as a Foundation to Develop Ethical Guidelines in African American Dementia Caregiving.

We applied natural language processing and topic modeling to publicly available abstracts and titles of 263 papers in the scientific literature mentioning AI and demographics (corpus 1 before Covid-19, corpus 2 after Covid-19) extracted from the MEDLINE database. We found exponential growth of AI studies mentioning demographics since the pandemic (Before Covid-19: N= 40 vs. After Covid-19: N= 223) [forecast model equation: ln(Number of Records) = 250.543*ln(Year) + -1904.38, p = 0.0005229]. Topics related to diagnostic imaging, quality of life, Covid, psychology, and smartphone increased during the pandemic, while cancer-related topics decreased. The application of topic modeling to the scientific literature on AI and demographics provides a foundation for the next steps regarding developing guidelines for the ethical use of AI for African American dementia caregivers.

Exploring Information Technology Industry Programming Language Trends with Non-Negative Matrix Factorization Topic Modelling

The study aimed to determine the preferred programming language among information technology professionals using non-negative matrix factorization topic modelling technique. The results showed that the majority of participants were software developers and programmers who commonly used C#, Java, and Python, and agreed that Java, C, or C++ would be the best language to start with when learning programming. The application of topic modelling revealed key themes such as online video tutorials as effective learning resources, hands-on activities as key to learning programming, importance of feedback, effective teaching strategies, and problem-solving skills as crucial for success in programming. Results of the study may serve as baseline data for the improvement of curricular offerings.

Overview of infection control in nursing research in Korea over the last 10 years: Text network analysis and topic modeling

Background: With the emergence of new infectious diseases, infection control nursing (ICN) in hospitals has become increasingly significant. Consequently, research on ICN has been actively performed. We examined the knowledge structure and trends addressed in Korean ICN research. Methods: From 5 web-based Korean academic databases (DBpia, KISS, KMbase, KoreaMed, and RISS), 2,244 studies published between 2013 and 2022 were retrieved using ICN-related search terms (eg, “nurse” or “nursing” along with “infection control,” “infection prevention,” “healthcare-associated infection,” or “standard precautions”). After deleting duplicates, the authors assessed titles and abstracts and included 250 research abstracts in this study. Using NetMiner 4.4 software (Cyram, Seoul, Korea), words from abstracts of published articles were extracted and refined, then text network analysis and topic modeling were performed. A text network was structured based on the co-occurrence matrix of key words (semantic morphemes) and was analyzed to identify the main key words. Through topic modeling using the Latent Dirichlet Allocation algorithm, latent topics in the research abstracts were extracted. The authors verified the key words comprising the topic and the result of classifying the documents by topic and named topics. Results: The number of studies, which increased following the outbreak of Middle East respiratory syndrome in 2015, has declined over time but peaked in 2021 with the COVID-19 pandemic. The text network composed of the key words of the research abstracts was generated and visualized (Fig. 1). As a result of text network analysis, the 5 most common key words were ‘nurse,’ ‘infection control,’ ‘nursing care,’ ‘practice,’ and ‘perception’ in terms of degree and betweenness centrality. Other prominent main keywords were also identified: ‘knowledge,’ ‘compliance,’ ‘education,’ ‘intervention,’ ‘intention,’ and ‘safety.’ With the application of topic modeling to the research abstracts, 5 topics were derived and named as follows (Fig. 2): “infection control in nursing care for patient safety,” “infection control measures for healthcare personnel safety,” “burdens and obstacles for infection control among nurses,” “infection control for multidrug-resistant organisms,” and “knowledge, attitude, practice for infection control among nurses.” Conclusions: By applying text-network analysis and topic modeling, we obtained insights into Korean ICN research trends. To explore global ICN research trends, further study is necessary to analyze internationally published studies reflecting each country’s nursing work conditions.Disclosure: None

Eliciting Insights From Chat Logs of the 25X5 Symposium to Reduce Documentation Burden: Novel Application of Topic Modeling.

Addressing clinician documentation burden through "targeted solutions" is a growing priority for many organizations ranging from government and academia to industry. Between January and February 2021, the 25 by 5: Symposium to Reduce Documentation Burden on US Clinicians by 75% (25X5 Symposium) convened across 2 weekly 2-hour sessions among experts and stakeholders to generate actionable goals for reducing clinician documentation over the next 5 years. Throughout this web-based symposium, we passively collected attendees' contributions to a chat functionality-with their knowledge that the content would be deidentified and made publicly available. This presented a novel opportunity to synthesize and understand participants' perceptions and interests from chat messages. We performed a content analysis of 25X5 Symposium chat logs to identify themes about reducing clinician documentation burden. The objective of this study was to explore unstructured chat log content from the web-based 25X5 Symposium to elicit latent insights on clinician documentation burden among clinicians, health care leaders, and other stakeholders using topic modeling. Across the 6 sessions, we captured 1787 messages among 167 unique chat participants cumulatively; 14 were private messages not included in the analysis. We implemented a latent Dirichlet allocation (LDA) topic model on the aggregated dataset to identify clinician documentation burden topics mentioned in the chat logs. Coherence scores and manual examination informed optimal model selection. Next, 5 domain experts independently and qualitatively assigned descriptive labels to model-identified topics and classified them into higher-level categories, which were finalized through a panel consensus. We uncovered ten topics using the LDA model: (1) determining data and documentation needs (422/1773, 23.8%); (2) collectively reassessing documentation requirements in electronic health records (EHRs) (252/1773, 14.2%); (3) focusing documentation on patient narrative (162/1773, 9.1%); (4) documentation that adds value (147/1773, 8.3%); (5) regulatory impact on clinician burden (142/1773, 8%); (6) improved EHR user interface and design (128/1773, 7.2%); (7) addressing poor usability (122/1773, 6.9%); (8) sharing 25X5 Symposium resources (122/1773, 6.9%); (9) capturing data related to clinician practice (113/1773, 6.4%); and (10) the role of quality measures and technology in burnout (110/1773, 6.2%). Among these 10 topics, 5 high-level categories emerged: consensus building (821/1773, 46.3%), burden sources (365/1773, 20.6%), EHR design (250/1773, 14.1%), patient-centered care (162/1773, 9.1%), and symposium comments (122/1773, 6.9%). We conducted a topic modeling analysis on 25X5 Symposium multiparticipant chat logs to explore the feasibility of this novel application and elicit additional insights on clinician documentation burden among attendees. Based on the results of our LDA analysis, consensus building, burden sources, EHR design, and patient-centered care may be important themes to consider when addressing clinician documentation burden. Our findings demonstrate the value of topic modeling in discovering topics associated with clinician documentation burden using unstructured textual content. Topic modeling may be a suitable approach to examine latent themes presented in web-based symposium chat logs.

Emergent Coding and Topic Modeling: A Comparison of Two Qualitative Analysis Methods on Teacher Focus Group Data

More than ever in the past, researchers have access to broad, educationally relevant text data from sources such as literature databases (e.g., ERIC), an open-ended response from online courses/surveys, online discussion forums, digital essays, and social media. These advances in data availability can dramatically increase the possibilities for discovering new patterns in the data and testing new theories through processing texts with emerging analytic techniques. In our study, we extended the application of Topic Modeling (TM) to data collected from focus groups within the context of a larger study. Specifically, we compared the results of emergent qualitative coding and TM. We found a high level of agreement between TM and emergent qualitative coding, suggesting TM is a viable method for coding focus group data when augmenting and validating manual qualitative coding. We also found that TM was ineffective in capturing more nuanced information than the qualitative coding was able to identify. This can be explained by two factors: (1) the word level tokenization we used in the study, and (2) variations in the terminology teachers used to identify the different technologies. Recommendations include additional data cleaning steps researchers should take and specifications within the topic modeling code when using topic modeling to analyze focus group data.

A Comparison of Different Topic Modeling Methods through a Real Case Study of Italian Customer Care

The paper deals with the analysis of conversation transcriptions between customers and agents in a call center of a customer care service. The objective is to support the analysis of text transcription of human-to-human conversations, to obtain reports on customer problems and complaints, and on the way an agent has solved them. The aim is to provide customer care service with a high level of efficiency and user satisfaction. To this aim, topic modeling is considered since it facilitates insightful analysis from large documents and datasets, such as a summarization of the main topics and topic characteristics. This paper presents a performance comparison of four topic modeling algorithms: (i) Latent Dirichlet Allocation (LDA); (ii) Non-negative Matrix Factorization (NMF); (iii) Neural-ProdLDA (Neural LDA) and Contextualized Topic Models (CTM). The comparison study is based on a database containing real conversation transcriptions in Italian Natural Language. Experimental results and different topic evaluation metrics are analyzed in this paper to determine the most suitable model for the case study. The gained knowledge can be exploited by practitioners to identify the optimal strategy and to perform and evaluate topic modeling on Italian natural language transcriptions of human-to-human conversations. This work can be an asset for grounding applications of topic modeling and can be inspiring for similar case studies in the domain of customer care quality.

Integrated Assessment of Teaching Efficacy: A Natural Language Processing Approach

The most significant component in the education domain is evaluation. Apart from student evaluation, teacher evaluation plays a vital role in the colleges or universities. The implementation of a scientific and appropriate assessment method for enhancing teaching standards in educational institutions is absolutely essential. Conventional teacher assessment techniques have always been bounded to bias and injustice for single dimensional assessment criteria, biased scoring, and ineffective integration. In this regard,it is crucial to develop a special-ized teacher evaluation assistant (TEA) system that integrates with some computational intelligence algorithms. This research concentrates on using Natural language processing(NLP) based techniques for empirically analysing teaching effectiveness. We develop a model in which a teacher is evaluated based on the content he delivers during a lecture. Two techniques are employed to evaluate teacher effectiveness using topic modelling and text clustering. By the application of topic modelling, an accuracy of 75% is achieved and text clustering achieved an accuracy of 80%. Thus, the method can effectively be deployed to assess and predict the effectiveness of a teacher's teaching.

Disentangling quarks and gluons in CMS open data

We study quark and gluon jets separately using public collider data from the CMS experiment. Our analysis is based on 2.3/fb of proton-proton collisions at 7 TeV, collected at the Large Hadron Collider in 2011. We define two non-overlapping samples via a pseudorapidity cut -- central jets with |eta| < 0.65 and forward jets with |eta| > 0.65 -- and employ jet topic modeling to extract individual distributions for the maximally separable categories. Under certain assumptions, such as sample independence and mutual irreducibility, these categories correspond to "quark" and "gluon" jets, as given by a recently proposed operational definition. We consider a number of different methods for extracting reducibility factors from the central and forward datasets, from which the fractions of quark jets in each sample can be determined. The greatest stability and robustness to statistical uncertainties is achieved by a novel method based on parametrizing the endpoints of a receiver operating characteristic (ROC) curve. To mitigate detector effects, which would otherwise induce unphysical differences between central and forward jets, we use the OmniFold method to perform central value unfolding. As a demonstration of the power of this method, we extract the intrinsic dimensionality of the quark and gluon jet samples, which exhibit Casimir scaling, as expected from the strongly-ordered limit. To our knowledge, this work is the first application of full phase space unfolding to real collider data, and one of the first applications of topic modeling to extract separate quark and gluon distributions at the LHC.