“…The applied research methods were classified based on the following aspects: (1) manual analysis methods (content analysis methodology, grounded theory, ethnography, classification analysis, thematic analysis, and scoring tables) and computer-aided analysis methods (latent Dirichlet allocation, support vector machine, probabilistic clustering, image analysis, topic modeling, sentiment analysis, and other natural language processing technologies), (2) categories of research contents, and (3) health care areas (health practice, health services, and health education). …”
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“…This data was used to establish a machine learning pipeline, which implemented the text representation methods bag-of-words (BOW), term frequency-inverse document frequency (TF-IDF), principal component analysis (PCA), non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), and document embedding (doc2vec). …”
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“…Unbiased analyses using a Dirichlet process Gaussian process (DPGP) method revealed clusters of genes temporally co-regulated and the genes within these clusters were distinct between SW016789 and thapsigargin treatments. …”
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“…Subsequently, we used latent Dirichlet allocation topic modeling to analyze the public’s focus as well as the bidirectional long short-term memory model to conduct emotional analysis. …”
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“…Analyses of frequency and intensity of user interaction in the community were performed together with a semantic analysis of the messages, done by a latent Dirichlet allocation method. The association between weight loss and online user activity patterns, the semantics of the messages, and real-world variables was found by a linear regression model using 30-day weight change as the dependent variable. …”
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“…Next, we used Latent Dirichlet Allocation (LDA) to characterize remaining topics within the Reddit data. …”
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“…We sought a suitable topic number with evidence of the coherence number. We operated latent Dirichlet allocation topic modeling with a suitable topic number and generated corresponding keywords and topic names. …”
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“…Topic analysis to determine the content of tweets was done using latent Dirichlet allocation in Python, using a Java implementation, LdaMallet, with Gensim wrapper. …”
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“…After segmenting the words of the collected text, we used a topic modeling technique, latent Dirichlet allocation (LDA), to identify the most common topics posted by users. …”
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“…First, we characterized the evolution of hashtags over time using latent Dirichlet allocation (LDA) topic modeling. Second, we increased the granularity of this analysis by downloading Twitter timelines of a large cohort of individuals (n=354,738) in 20 major US cities to assess changes in social media use. …”
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“…After preprocessing the data, we tested Latent Dirichlet Allocation models to identify topics associated with these tweets. …”
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“…The topic distributions were obtained using the latent Dirichlet allocation algorithm. We defined a measure of topic cohesion and described the most cohesive topics. …”
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“…We determined appropriate topic numbers for these two periods based on the coherence score and applied latent Dirichlet allocation topic modeling. Keywords for each topic and corresponding topics’ names were then generated. …”
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“…Open-ended responses were analyzed using the latent Dirichlet allocation method to uncover semantic topics. …”
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“…The four taxonomic classes that change most from before to after treatment are shown with the mean and confidence intervals based on fitting OTU data to a Dirichlet multinomial distribution. Right panel, MHI biomarker for the same time points and investigational product groups, shown as median (red) and individual samples. …”
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“…Themes discovered through latent Dirichlet allocation (LDA), an unsupervised topic modeling technique, were assessed and compared with the predetermined themes of interest. …”
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“…We identified popular discussion topics using latent Dirichlet allocation. We further examined changes in public sentiment and topics during the 3 stages of the vaccination timeline. …”
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“…The tweets’ main topics were discovered using Latent Dirichlet Allocation in the sklearn library. The pysentimiento package was used to find the sentiments of English and Spanish posts, and the CamemBERT package was used to recognize the sentiments of French posts. …”
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“…Second, we applied the well-known topic extraction algorithm Latent Dirichlet Allocation to more than 500,000 textual reviews from over 75,000 Chinese doctors across four major specialty areas to understand what Chinese health consumers said online about their doctor visits. …”
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“…We used the Bing sentiment library for a sentiment analysis of interview transcripts and then applied NLP-based latent Dirichlet allocation (LDA) topic modeling to identify differences and similarities in patient and clinician participant interviews. …”
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