“…Also, were tested three models, an embedding vectors approach with Doc2Vec model, a probabilistic model with Latent Dirichlet Allocation, and a classification task approach with a Convolutional Neural Network used on the final architecture. …”
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“…We developed a novel topic model based on latent Dirichlet allocation, affinity propagation clustering and the PageRank algorithm (LDAPR) to extract knowledge from biosafety research publications from 2011 to 2020. …”
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“…We then apply a dynamic topic modeling algorithm based on the Latent Dirichlet allocation (LDA) algorithm to assess how topics cluster based on topics. …”
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“…We show that in a model in which membership coefficient vectors in a population follow a Dirichlet distribution, the measure increases linearly with a parameter describing the variance of a specified component of the membership vector and does not depend on its mean. …”
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“…After calculating the number of topics, Latent Dirichlet Allocation (LDA) model is used to extract topics and keywords. …”
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“…For diseases and traits that show spiked signals on the Manhattan plots, the latent Dirichlet process regression is preferred, as it can flexibly accommodate both the oligogenic and omnigenic models. …”
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“…First, using dynamic topic clustering on Weibo through the latent Dirichlet allocation (LDA) model, a total of 118 topics were generated in 24 months using 2,211,806 microblog posts. …”
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“…In other words, the Latent Dirichlet Allocation topic model was used to categorize the major travel-related subjects of forum postings, after which the most relevant search terms for each topic were determined. …”
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“…To do so, we use support vector regression with sequential minimal optimization (SMO), latent Dirichlet allocation (LDA), and k-means approaches to develop the hybrid method. …”
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“…Topic modeling using the Latent Dirichlet Allocation model was conducted to monitor users' discussions surrounding mental health concerns. …”
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“…Initially, a qualitative research method based on interviews followed by text-mining has been used followed by analysis using python for topic modelling using Latent Dirichlet Allocation (LDA). The findings revealed several prominent themes like consumer shift to online buying, two contrasting spending intentions namely financial security and compensatory consumptions, irrational panic buying, uncertainty/ambiguity of government protocol and norms, social media fraudulent practices and misinformation dissemination, personalized buying experience, reduced trust on news and marketers, logistics and transportation bottlenecks, labor shortage due to migration and plant closures, and bullwhip effect in supply chains.…”
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“…We employed the ANOVA and Dirichlet Process mixtures of Generalized Linear Models (DP-GLM) in order to explain the causes of stress and anxiety after grouping variables represented by gender and the study specializations. …”
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“…To this end, we identify the latent topics in volunteerism-related microblogs on Weibo, the Chinese equivalent of Twitter using the topic modeling analysis via Latent Dirichlet Allocation (LDA). To further investigate the public sentiment toward the topics generated by LDA, we also conducted sentiment analysis on the sample posts using the open-source natural language processing (NLP) technique from Baidu. …”
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“…DESIGN: Blood samples from 70 participants with body mass index (BMI) ​≥ ​30kg/m2 and age ≥55 years, with (cases) or without (controls) hand plus knee OA, were analyzed for serum LPS and composition of the plasma microbiome. The Dirichlet-multinominal recursive partitioning model (DM-RPart) was applied to microbiome compositional data to test the hypothesis that LPS levels distinguish plasma microbiome, accounting for BMI and age. …”
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“…The modeling was performed by combining the latent Dirichlet allocation (LDA) algorithm with ParsBERT. …”
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“…Specifically, topic modelling, operationalised using Latent Dirichlet Allocation (LDA) models, was used to discern discussion topics in 189,658 tweets about ENDS (collected April–December 2019). …”
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“…We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information by deploying topic modeling, based on Latent Dirichlet Allocation, in order to extract feature vectors from a collection of tweets and financial news. …”
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“…These features are evaluated by several types of machine learning for classical Arabic, for example, support vector machines (SVM), Logistic Regression (LR), Naive Bayes (NB) K-Nearest Neighbors (KNN), Latent Dirichlet Allocation (LDA) and Classification And Regression Trees (CART). …”
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“…A topic network was constructed by latent Dirichlet allocation (LDA) and the Louvain algorithm. …”
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“…After the defined filtering process, we retrieved 2,433 articles from Web of Science, ERIC, and PsycInfo databases and employed Latent Dirichlet Allocation (LDA) topic modeling to extract key terms and topics from the selected articles. …”
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