HLDA based text clustering

Research publications related to the novel coronavirus disease COVID-19 are rapidly growing in number. However, current online literature hubs, even with artificial intelligence, are inadequate for identifying the relative strength of research topics. Hence, we aimed to develop a comprehensive Latent Dirichlet Allocation (LDA) topic model using natural language processing (NLP) techniques, provide visualisations for temporal trends, and apply our methodology to improve existing online literature hubs.Using the search term “COVID”, abstracts were extracted from PubMed®, from January to July 2020 (N=16346). An LDA topic model was trained on 81% of abstracts. Weekly temporal trends were visualised as a heatmap on all abstracts. Then, we tested our methodology on over 23,000 abstracts gathered from January 2020 to September 2020 from LitCovid, a literature hub from the National Center for Biotechnology Information. We use our topic model to subdivide LitCovid’s eight categories into corresponding LDA topics.The optimised LDA topic model, created using PubMed® data, produced 25 comprehensive topics with no significant overlap. There were temporal changes for topics: prominence of “Mental Health” and “Socioeconomic Impact” increased, “Genome Sequence” decreased, and “Epidemiology” remained relatively constant. We identified inadequate representation of “Airborne Transmission Protection”. Importantly, research on masks and PPE is skewed towards clinical applications with a lack of population-based epidemiological research. Our methodology, when applied to LitCovid, identified important topics within each LitCovid category. For example, “Case Report” was split into topics such as “Pulmonary” and “Oncology” as well as the under-represented topics “Haematology” and “Gastroenterology”. Our work allows for comprehensive topic identification and intuitive visualisation of temporal trends in COVID-19 research. Implementation of the methodology complements existing online literature hubs and identifies underrepresented topics such as population-based studies on masks that may be of significant public interest.Funding Statement: None to declare.Declaration of Interests: There are no conflicts of interest.

To derive latent topics from free-text responses on the negative impact of the pandemic on research activities and determine similarities and differences in the resulting themes between academic-based and clinical-based researchers. We performed a secondary analysis of free-text responses from a cross-sectional online survey conducted by the Japan Academy of Nursing Science of its members in early 2020. The participants were categorized into two groups by workplace (academic-based and clinical-based researchers). Latent Dirichlet allocation (LDA) topic modeling was used to extract latent topics statistically and list important keywords/text associated with the topics. After organizing similar topics by principal component analysis (PCA), we finally derived topic-associated themes by reading the keywords/texts and determining the similarity and differences of the themes between the two groups. A total of 201 respondents (163 academic-based and 38 clinical-based researchers) provided free-text responses. LDA identified eight and three latent topics for the academic-based and clinical-based researchers, respectively. While PCA re-grouped the eight topics derived from the former group into four themes, no merging of the topics from the latter group was performed resulting in three themes. The only theme common to the two groups was "barriers to conducting research," with the remaining themes differing between the groups. Using LDA topic modeling with PCA, we identified similarities and differences in the themes described in free-text responses about the negative impact of the pandemic between academic-based and clinical-based researchers. Measures to mitigate the negative impact of pandemics on nursing research may need to be tailored separately.

This study employs the Latent Dirichlet Allocation (LDA) topic model to extract and analyze the evolution of factors influencing enterprise resilience. By applying LDA to 318 resilience-related literature, optimal topics were identified based on topic confusion scores and pyLDAvis visualization results. High-frequency keywords were used to identify key themes and summarize their evolutionary patterns. Four core themes were distilled regarding enterprise resilience mechanisms: “Digital Transformation Drives Corporate Resilience (Topic 1),” “Financial Policies Empower Corporate Resilience (Topic 2),” “Green Innovation Promotes Corporate Resilience (Topic 3),”, “Artificial Intelligence Factors Boost Industrial Chain Resilience (Topic 4),”. Among these, “Digital Transformation Drives Corporate Resilience (Topic 1),” represents a current hot research direction. This study reveals the patterns by which different key factors enhance corporate resilience, providing theoretical foundations for corporate strategy formulation.

Topic models are widely used to discover the latent representation of a set of documents. The two canonical models are latent Dirichlet allocation, and Gaussian latent Dirichlet allocation, where the former uses multinomial distributions over words, and the latter uses multivariate Gaussian distributions over pre-trained word embedding vectors as the latent topic representations, respectively. Compared with latent Dirichlet allocation, Gaussian latent Dirichlet allocation is limited in the sense that it does not capture the polysemy of a word such as "bank." In this paper, we show that Gaussian latent Dirichlet allocation could recover the ability to capture polysemy by introducing a hierarchical structure in the set of topics that the model can use to represent a given document. Our Gaussian hierarchical latent Dirichlet allocation significantly improves polysemy detection compared with Gaussian-based models and provides more parsimonious topic representations compared with hierarchical latent Dirichlet allocation. Our extensive quantitative experiments show that our model also achieves better topic coherence and held-out document predictive accuracy over a wide range of corpus and word embedding vectors which significantly improves the capture of polysemy compared with GLDA and CGTM. Our model learns the underlying topic distribution and hierarchical structure among topics simultaneously, which can be further used to understand the correlation among topics. Moreover, the added flexibility of our model does not necessarily increase the time complexity compared with GLDA and CGTM, which makes our model a good competitor to GLDA.

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.

Despite the unique characteristics of urban forests, the motivating factors of urban forest visitors have not been clearly differentiated from other types of the forest resource. This study aims to identify the motivating factors of urban forest visitors, using latent Dirichlet allocation (LDA) topic modeling based on social big data. A total of 57,449 cases of social text data from social blogs containing the keyword “urban forest” were collected from Naver and Daum, the major search engines in South Korea. Then, 17,229 cases were excluded using morpheme analysis and stop word elimination; 40,110 cases were analyzed to identify the motivating factors of urban forest visitors through LDA topic modeling. Seven motivating factors—“Cafe-related Walk”, “Healing Trip”, “Daily Leisure”, “Family Trip”, “Wonderful View”, “Clean Space”, and “Exhibition and Photography”—were extracted; each contained five keywords. This study elucidates the role of forests as a place for healing, leisure, and daily exercise. The results suggest that efforts should be made toward developing various programs regarding the basic functionality of urban forests as a natural resource and a unique place to support a diversity of leisure and cultural activities.

This study utilizes LDA topic modeling to examine research trends related to COVID-19 within the field of social work, analyzing these trends through peer-reviewed articles from the Korea Citation Index database. Latent Dirichlet Allocation (LDA) topic modeling, a statistical method for discovering abstract topics within a collection of documents, is applied to categorize and summarize the thematic concentration of the literature. Five themes have emerged: healthcare service and digitalized methods, exploring mental health status, qualitative approaches to social service program responses to COVID-19, evaluation of care services, and public service and program responses to COVID-19. Key findings reveal that the Korean social work academia focused on digital-based non-face-to-face services, evaluating the adequacy of public social services, and analyzing mental health and caregiving services during the pandemic. These findings indicate a reassessment of social work practices in response to the pandemic, underscoring the need to explore the challenges and opportunities presented by varied national responses. Additionally, the application of Python and machine learning in this research has shown significant benefits for social work studies, enabling a deeper analysis of complex, big data and facilitating more informed decision-making.

While the widespread adoption of intelligent customer service systems has significantly boosted service efficiency, the persistent surge in complaints has revealed notable service failure challenges. This study addresses the shortcomings of current analytical approaches, which suffer from inefficiency and high subjectivity. To surmount these obstacles, we utilized the Latent Dirichlet Allocation (LDA) topic model to perform a thorough analysis of 1,247 genuine complaint texts. By means of dual assessment using the perplexity metric and coherence score, we determined that the optimal number of topics is three. We pinpointed three primary issues: functional failure, procedural failure, and emotional failure, which account for 42%, 35%, and 23% of the total, respectively. Our research uncovers that the root causes of intelligent customer service failures lie in the discrepancy between technological capabilities and user expectations, the clash between cost-cutting and efficiency-enhancement measures versus service quality, as well as a bias in system design that favors efficiency over a user-centric approach. Drawing on these insights, this paper puts forward tailored improvement strategies, including enriching the knowledge base, streamlining the process for escalating to human support, and integrating sentiment analysis capabilities. These strategies aim to furnish enterprises with a theoretical foundation and practical guidance for refining their intelligent customer service systems.

Research on multimodal data analysis such as annotated image analysis is becoming more important than ever due to the increase in the amount of data. One of the approaches to this problem is multimodal topic models as an extension of latent Dirichlet allocation (LDA). Symmetric correspondence topic models (SymCorrLDA) are state-of-the-art multimodal topic models that can appropriately model multimodal data considering inter-modal dependencies. Incidentally, hierarchically structured categories can help users find relevant data from a large amount of data collection. Hierarchical topic models such as hierarchical latent Dirichlet allocation (hLDA) can discover a tree-structured hierarchy of latent topics from a given unimodal data collection; however, no hierarchical topic models can appropriately handle multimodal data considering intermodal mutual dependencies. In this paper, we propose h-SymCorrLDA to discover latent topic hierarchies from multimodal data by combining the ideas of the two previously mentioned models: multimodal topic models and hierarchical topic models. We demonstrate the effectiveness of our model compared with several baseline models through experiments with two datasets of annotated images.

We present a new way called Persona Analysis with Text Topic Modelling (PATTM), which tries to learn the role of personae according to the literal descriptions. It is similar to Latent Dirichlet Allocation (LDA) and Author Topic (AT) model, with the attribute of allowing all text to join in the topic modelling process, even when there is no persona in the text. We experiment on the “Libya Event” data set which contains more than 4,000 texts collected from the Internet. The PATTM gives lower perplexity than LDA and AT model on the data set.

Locally discriminative topic modeling

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.

Topic modeling approaches, such as Latent Dirichlet Allocation (LDA) and Hierarchical LDA (hLDA) have been used extensively to discover topics in various corpora. Unfortunately, these approaches do not perform well when applied to collections of social media posts. Further, these approaches do not allow users to focus topic discovery around subjectively interesting concepts. We propose the new Semi-Supervised Microblog-hLDA (SS-Micro-hLDA) model to discover topic hierarchies in short, noisy microblog documents in a way that allows users to focus topic discovery around interesting areas. We test SS-Micro-hLDA using a large, public collection of Twitter messages and Reddit social blogging site and show that our model outperforms hLDA, Constrained-hLDA, Recursive-rCRP and TSSB in terms of Pointwise Mutual Information (PMI) Score. Further, we test our model in terms of information entropy of held-out data and show that the new approach produces highly focused topic hierarchies.

BackgroundSexually transmitted diseases (STDs) are common and costly, impacting approximately 1 in 5 people annually. Reddit, the sixth most used internet site in the world, is a user-generated social media discussion platform that may be useful in monitoring discussion about STD symptoms and exposure.ObjectiveThis study sought to define and identify patterns and insights into STD-related discussions on Reddit over the course of the COVID-19 pandemic.MethodsWe extracted posts from Reddit from March 2019 through July 2021. We used a topic modeling method, Latent Dirichlet Allocation, to identify the most common topics discussed in the Reddit posts. We then used word clouds, qualitative topic labeling, and spline regression to characterize the content and distribution of the topics observed.ResultsOur extraction resulted in 24,311 total posts. Latent Dirichlet Allocation topic modeling showed that with 8 topics for each time period, we achieved high coherence values (pre–COVID-19=0.41, prevaccination=0.42, and postvaccination=0.44). Although most topic categories remained the same over time, the relative proportion of topics changed and new topics emerged. Spline regression revealed that some key terms had variability in the percentage of posts that coincided with pre–COVID-19 and post–COVID-19 periods, whereas others were uniform across the study periods.ConclusionsOur study’s use of Reddit is a novel way to gain insights into STD symptoms experienced, potential exposures, testing decisions, common questions, and behavior patterns (eg, during lockdown periods). For example, reduction in STD screening may result in observed negative health outcomes due to missed cases, which also impacts onward transmission. As Reddit use is anonymous, users may discuss sensitive topics with greater detail and more freely than in clinical encounters. Data from anonymous Reddit posts may be leveraged to enhance the understanding of the distribution of disease and need for targeted outreach or screening programs. This study provides evidence in favor of establishing Reddit as having feasibility and utility to enhance the understanding of sexual behaviors, STD experiences, and needed health engagement with the public.

In this paper we extend the latent Dirichlet allocation (LDA) topic model to model facial expression dynamics. Our topic model integrates the temporal information of image sequences through redefining topic generation probability without involving new latent variables or increasing inference difficulties. A collapsed Gibbs sampler is derived for batch learning with labeled training dataset and an efficient learning method for testing data is also discussed. We describe the resulting temporal latent topic model (TLTM) in detail and show how it can be applied to facial expression recognition. Experiments on CMU expression database illustrate that the proposed TLTM is very efficient in facial expression recognition.