{"id":25009,"date":"2021-04-14T23:10:04","date_gmt":"2021-04-15T03:10:04","guid":{"rendered":"http:\/\/bangla.salearningschool.com\/recent-posts\/lda-latent-dirichlet-allocation-lda-applications-of-lda-root\/"},"modified":"2021-04-14T23:10:04","modified_gmt":"2021-04-15T03:10:04","slug":"lda-latent-dirichlet-allocation-lda-applications-of-lda-root","status":"publish","type":"post","link":"http:\/\/bangla.sitestree.com\/?p=25009","title":{"rendered":"LDA: Latent Dirichlet Allocation (LDA): Applications of LDA #Root"},"content":{"rendered":"

What is LDA topic modeling?<\/p>\n

"Latent Dirichlet Allocation (LDA) is an example of topic model and is used to classify text in a document to a particular topic"<\/p>\n

https:\/\/towardsdatascience.com\/topic-modeling-and-latent-dirichlet-allocation-in-python-9bf156893c24<\/a><\/p>\n

What is topic modeling used for?
\n"In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body."<\/p>\n

Topic model – Wikipedia
\nhttps:\/\/en.m.wikipedia.org\/wiki\/Topic_model<\/a><\/p>\n

Is LDA supervised or unsupervised?
\n"Original LDA is unsupervised learning algorithm, while Labeled-LDA and Multi-Grain LDA, another topic model for classification and sentiment analysis, are supervised algorithm."<\/p>\n

https:\/\/www.quora.com\/Is-LDA-latent-dirichlet-allocation-unsupervised-or-supervised-learning<\/a><\/p>\n

Modeling healthcare data using multiple-channel latent Dirichlet allocation
\nhttps:\/\/www.sciencedirect.com\/science\/article\/pii\/S1532046416000253<\/a><\/p>\n

Latent Dirichlet Allocation for Classification using Gene Expression Data
\nhttps:\/\/ieeexplore.ieee.org\/document\/8251263<\/a><\/p>\n

A hybrid LDA and genetic algorithm for gene selection and classification of microarray data
\nhttps:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231210002456<\/a><\/p>\n

LDA and Magazine Similarity
\n–https:\/\/www.youtube.com\/watch?v=3mHy4OSyRf0<\/a><\/p>\n

LDA Applications on Scripts from Friends
\n–https:\/\/medium.com\/@sherryqixuan\/topic-modeling-and-pyldavis-visualization-86a543e21f58<\/a>
\n–https:\/\/towardsdatascience.com\/topic-modeling-and-latent-dirichlet-allocation-in-python9bf156893c24<\/a><\/p>\n

LDA on Kaggle Dataset
\n–https:\/\/www.kaggle.com\/therohk\/million-headlines\/data<\/a>
\n–https:\/\/gist.github.com\/davidandrzej\/939840<\/a> From: https:\/\/sitestree.com\/lda-latent-dirichlet-allocation-lda-applications-of-lda\/
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Post Data:2019-05-16 12:07:27<\/p>\n

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What is LDA topic modeling? "Latent Dirichlet Allocation (LDA) is an example of topic model and is used to classify text in a document to a particular topic" https:\/\/towardsdatascience.com\/topic-modeling-and-latent-dirichlet-allocation-in-python-9bf156893c24 What is topic modeling used for? "In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract … <\/p>\n

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\"Latent Dirichlet Allocation (LDA) is an example of topic model and is used to classify text in a document to a particular topic\" https:\/\/towardsdatascience.com\/topic-modeling-and-latent-dirichlet-allocation-in-python-9bf156893c24 What is topic modeling used for? \"In machine learning and natural language processing, a topic model is a type of statistical\u2026","rel":"","context":"In "FromSitesTree.com"","block_context":{"text":"FromSitesTree.com","link":"http:\/\/bangla.sitestree.com\/?cat=1917"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":78195,"url":"http:\/\/bangla.sitestree.com\/?p=78195","url_meta":{"origin":25009,"position":3},"title":"Dimensionality Reduction","author":"Sayed","date":"May 21, 2025","format":false,"excerpt":"Some Approaches \u2022Feature Selection \u2022Feature Extraction \u2022PCA \u2022SVD \u2022LDA Data Analytics, Machine Learning, Data Science","rel":"","context":"In "Analytics and Machine Learning Project Development"","block_context":{"text":"Analytics and Machine Learning Project Development","link":"http:\/\/bangla.sitestree.com\/?cat=1974"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/bangla.sitestree.com\/wp-content\/uploads\/2025\/05\/image-15.png?resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/bangla.sitestree.com\/wp-content\/uploads\/2025\/05\/image-15.png?resize=350%2C200 1x, https:\/\/i0.wp.com\/bangla.sitestree.com\/wp-content\/uploads\/2025\/05\/image-15.png?resize=525%2C300 1.5x, https:\/\/i0.wp.com\/bangla.sitestree.com\/wp-content\/uploads\/2025\/05\/image-15.png?resize=700%2C400 2x"},"classes":[]},{"id":24594,"url":"http:\/\/bangla.sitestree.com\/?p=24594","url_meta":{"origin":25009,"position":4},"title":"Topic 2: Example: Show Misc info about a Machine #Root","author":"Author-Check- Article-or-Video","date":"April 12, 2021","format":false,"excerpt":"bash-3.2$ cat echoscript.sh #!\/bin\/sh # This script will print useful information about the current machine. # It will print the machine name, model type, specific processor # information, and the current number of processes running. echo echo \"The name of the machine is c\" uname -n # uname -n gives\u2026","rel":"","context":"In "FromSitesTree.com"","block_context":{"text":"FromSitesTree.com","link":"http:\/\/bangla.sitestree.com\/?cat=1917"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":16619,"url":"http:\/\/bangla.sitestree.com\/?p=16619","url_meta":{"origin":25009,"position":5},"title":"Math\/Stat\/CS\/DS Topics that you need to know (with Cognitive, Psychomotor, Affective domain skills) to become a true and great Data Scientist","author":"Sayed","date":"January 7, 2020","format":false,"excerpt":"\"The core topics are cross-validation, shrinkage methods (ridge regression, the LASSO, etc.), neural networks, gradient boosting, separating hyperplanes, support vector machines, basis expansion and regularization (e.g., smoothing splines, wavelet smoothing, kernel smoothing), generalized additive models, bump hunting, multivariate adaptive regression splines (MARS), self-organizing maps, mixture model-based clustering, ensemble learning, and\u2026","rel":"","context":"In "Math and Statistics for Data Science, and Engineering"","block_context":{"text":"Math and Statistics for Data Science, and Engineering","link":"http:\/\/bangla.sitestree.com\/?cat=1908"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]}],"_links":{"self":[{"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=\/wp\/v2\/posts\/25009","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=25009"}],"version-history":[{"count":0,"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=\/wp\/v2\/posts\/25009\/revisions"}],"wp:attachment":[{"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=25009"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=25009"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=25009"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}