Augmented Latent Dirichlet Allocation (Lda) Topic Model with Gaussian Mixture Topics

This study aimed to assess the different needs of patients with breast cancer and their families in online health communities at different treatment phases using a Latent Dirichlet Allocation (LDA) model. Using Python, breast cancer-related posts were collected from two online health communities: patient-to-patient and patient-to-doctor. After data cleaning, eligible posts were categorized based on the treatment phase. Subsequently, an LDA model identifying the distinct need-related topics for each phase of treatment, including data preprocessing and LDA topic modeling, was established. Additionally, the demographic and interactive features of the posts were manually analyzed. We collected 84,043 posts, of which 9504 posts were included after data cleaning. Early diagnosis and rehabilitation treatment phases had the highest and lowest number of posts, respectively. LDA identified 11 topics: three in the initial diagnosis phase and two in each of the remaining treatment phases. The topics included disease outcomes, diagnosis analysis, treatment information, and emotional support in the initial diagnosis phase; surgical options and outcomes, postoperative care, and treatment planning in the perioperative treatment phase; treatment options and costs, side effects management, and disease prognosis assessment in the non-operative treatment phase; diagnosis and treatment options, disease prognosis, and emotional support in the relapse and metastasis treatment phase; and follow-up and recurrence concerns, physical symptoms, and lifestyle adjustments in the rehabilitation treatment phase. The needs of patients with breast cancer and their families differ across various phases of cancer therapy. Therefore, specific information or emotional assistance should be tailored to each phase of treatment based on the unique needs of patients and their families.

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.

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.

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.

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.

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.

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.

The diachronic nature of broadcast news data leads to the problem of out-of-vocabulary (OOV) words in large vocabulary continuous speech recognition (LVCSR) systems. Analysis of OOV words reveals that a majority of them are proper names (PNs). However, PNs are important for automatic indexing of audio–video content and for obtaining reliable automatic transcriptions. In this paper, we focus on the problem of OOV PNs in diachronic audio documents. To enable the recovery of the PNs missed by the LVCSR system, relevant OOV PNs are retrieved by exploiting the semantic context of the LVCSR transcriptions. For retrieval of OOV PNs, we explore topic and semantic context derived from latent Dirichlet allocation (LDA) topic models, continuous word vector representations and the neural bag-of-words (NBOW) model which is capable of learning task specific word and context representations. We propose a neural bag-of-weighted words (NBOW2) model which learns to assign higher weights to words that are important for retrieval of an OOV PN. With experiments on French broadcast news videos, we show that the NBOW and NBOW2 models outperform the methods based on raw embeddings from LDA and Skip-gram models. Combining the NBOW and NBOW2 models gives a faster convergence during training. Second pass speech recognition experiments, in which the LVCSR vocabulary and language model are updated with the retrieved OOV PNs, demonstrate the effectiveness of the proposed context models.

A text mining approach based on latent Dirichlet allocation (LDA) is proposed to analyze the competency characteristics. First, we briefly introduce the principle and hypothesis of latent Dirichlet allocation (LDA) model. Second, we elaborate the idea of using LDA topic model to extract competency, Then, we use Chinese text materials such as biographies and interviews of Chinese scientific and technological talents as data sources to conduct experiments, and obtain four competency topics including knowledge, attitude, quality and values. The research results preliminarily verify the effectiveness of latent Dirichlet allocation (LDA) model in competency research, but there are still many details to be improved in the future.

This study investigated the evolution of library and information science (LIS) by analyzing research topics in LIS journal articles. The analysis is divided into five periods covering the years 1996–2019. Latent Dirichlet allocation modeling was used to identify underlying topics based on 14,035 documents. An improved data-selection method was devised in order to generate a dynamic journal list that included influential journals for each period. Results indicate that (a) library science has become less prevalent over time, as there are no top topic clusters relevant to library issues since the period 2000–2005; (b) bibliometrics, especially citation analysis, is highly stable across periods, as reflected by the stable subclusters and consistent keywords; and (c) information retrieval has consistently been the dominant domain with interests gradually shifting to model-based text processing. Information seeking and behavior is also a stable field that tends to be dispersed among various topics rather than presented as its own subject. Information systems and organizational activities have been continuously discussed and have developed a closer relationship with e-commerce. Topics that occurred only once have undergone a change of technological context from the networks and Internet to social media and mobile applications.

Topic models have recently shown to be really useful tools for the analysis of microarray experiments. In particular they have been successfully applied to gene clustering and, very recently, also to samples classification. In this latter case, nevertheless, the basic assumption of functional independence between genes is limiting, since many other a priori information about genes’ interactions may be available (co-regulation, spatial proximity or other a priori knowledge). In this paper a novel topic model is proposed, which enriches and extends the Latent Dirichlet Allocation (LDA) model by integrating such dependencies, encoded in a categorization of genes. The proposed topic model is used to derive a highly informative and discriminant representation for microarray experiments. Its usefulness, in comparison with standard topic models, has been demonstrated in two different classification tests.KeywordsTopic ModelLatent Dirichlet AllocationMultinomial DistributionProbabilistic Latent Semantic AnalysisLatent Dirichlet Allocation ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The study aims to investigate the integration of artificial intelligence technology into Weibo sentiment analysis, aiming to enhance the effectiveness of Weibo in human-computer interaction education. Initially, the Weibo sentiment dictionary is created, and a conventional model for sentiment analysis of user-forwarded Weibo is introduced, specifically the Latent Dirichlet Allocation (LDA) model. Then, the deep learning models in the field of artificial intelligence, namely, the convolutional neural network (CNN) model and the long short-term memory network (LSTM) model, are proposed. Tencent Weibo data set is obtained through Application Programming Interface (API) crawler. The experimental environment of the deep learning model is analyzed and the data set is preprocessed. The results show that when the number of topics is 120, the relative maximum value of F1 is 69.92% and 69.96% with and without the introduction of emotional features in the LDA model, respectively. The accuracy of CNN model and LSTM model is 0.793 and 0.849, respectively. In the three cases of user characteristics, user characteristics + Weibo features, and use characteristics + Weibo features + relationship characteristics, the polarity of the forwarded comments of the LDA model doesn’t change much. In conclusion, the LDA model demonstrates universality and accuracy in sentiment analysis of user-forwarded Weibo, while LSTM proves to be more suitable for sentiment classification in this context. Leveraging the LDA deep learning model, LSTM effectively analyzes the sentiment of users forwarding Weibo. These findings serve as an experimental foundation for the efficient integration of Weibo in human-computer interaction education.

Majority of people now search the internet for news or information topics. The growth of the internet and social media has led to the emergence of hundreds of portals or online news with very diverse news topics. Searching for headlines manually is an ineffective and time-consuming method. In this study headlines modeling was used using Latent Dirichlet Allocation (LDA). Prior to the application of the LDA model, supporting processes such as tokenization, lemmatization, tf-idf factorization and non-negative matrix factorization were also applied. The results showed that the LDA can be applied to model the news topic well with a loglikelihood score of -13615,912 and a perplexity score of 378,958. In addition to using LDA, topic modeling is also done in the form of clusters by applying k-means clustering. With the elbow method, the ideal number of clusters for k-means clustering is 5 clusters and the silhouette performance is 0.62