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Skip search results from other journals and go to results- 8 Journal of Medical Internet Research
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Public Attitudes Toward Violence Against Doctors: Sentiment Analysis of Chinese Users
It is calculated as follows:
The TF-IDF weights are ultimately formed by the product of TF and IDF:
LDA is a probabilistic graphical model to identify hidden thematic structures within text collections. The model was proposed by Blei et al [38] in 2003 and has become a classic method for topic modeling in text mining. The LDA model extracts potential topics from documents or corpora using a bag-of-words approach.
JMIR Med Inform 2025;13:e63772
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To uncover the subthemes underneath the online news for RQ1, LDA was first performed on the combined dataset to investigate the “hidden” thematic topics. The distribution of these topics will be then examined in each dataset separately to allow a more nuanced understanding.
J Med Internet Res 2025;27:e66696
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One of the most important types of these algorithms is latent Dirichlet allocation (LDA) [25]. The LDA model is a generative model used in natural language study, which allows for extracting topics from a set of source documents. Each document is considered a group of words that form one or more latent topics when combined [26]. A particular distribution of terms characterizes each topic. LDA's generative process is based on analyzing the text's data (text mining).
J Med Internet Res 2025;27:e58656
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Open-ended questions relating to dashboard experience were also analyzed using latent Dirichlet allocation (LDA) [14] to identify themes and keywords. LDA is a probabilistic topic modeling technique used to identify clusters of co-occurring words in text data. We used MALLET implementation [15] of LDA to generate topics in an unsupervised manner.
JMIR Ment Health 2024;11:e59785
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This study used the Latent Dirichlet Allocation (LDA) model for topic modeling. LDA is the most widely used topic model at the time and could be used to identify the most common topics across social media platforms [30]. LDA is an unsupervised learning algorithm that does not require prelabeled datasets, making it suitable for handling large and diverse text data such as Weibo posts [31].
J Med Internet Res 2024;26:e58518
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Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study
Drawing from the metadata of publications retrieved from Pub Med, we used latent Dirichlet allocation (LDA) topic modeling—an established method in various academic research fields, including technology management, computer science, and biomedicine [32-34]—to discern shifts in research areas within individual AI domains. Our LDA topic modeling, conducted in Python using the Gensim library, used unsupervised machine learning to analyze vast quantities of unstructured data.
J Med Internet Res 2023;25:e45815
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Since LDA identifies patterns via co-occurrences of different words, we are especially interested in words that have at least one independent semantic meaning. Thus, words that are punctuation marks, stop words, and hyperlinks were removed from each document. After those words were removed, we ran the LDA topic model to find the underlying topic model structure for each corpus.
More formally, LDA is defined as a generative probabilistic model of a corpus [24].
J Med Internet Res 2023;25:e48405
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We used latent Dirichlet allocation (LDA), a popular topic modeling algorithm proposed by Blei et al in 2003 [38], to extract public opinions from posts. LDA is an unsupervised text classification algorithm based on a 3-layer Bayesian structure that maps the relationship between topics, documents (posts in this study), and words [39].
First, we sorted all the posts chronologically to show how public opinion on COVID-19 vaccination evolved.
J Med Internet Res 2023;25:e42671
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Uncovering the Reasons Behind COVID-19 Vaccine Hesitancy in Serbia: Sentiment-Based Topic Modeling
Compared to LDA, which gives more general descriptions of broader topics [46], the architecture of NMF enables it to find more detailed, clear-cut, and coherent topics [37,46,47]. Chen et al [18] even claim that NMF can learn from data similarly to the way humans do, which makes its results more easily interpretable than in the case of LDA.
J Med Internet Res 2022;24(11):e42261
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Several practical guides have been published [14-17] that broadly outline several different ways to approach LDA, using a variety of packages. Broadly, training an LDA model involves 3 major steps: data selection, data preprocessing, and data analysis (Figure 1). However, these are not prescriptive, and individual applications of LDA may involve iterations of these steps.
Summary of latent Dirichlet allocation (LDA) data selection, preprocessing, and analysis steps.
J Med Internet Res 2022;24(11):e33166
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