Is there any way I can map generated topic from LDA to the list of documents and identify to which topic it belongs to ? I am interested in clustering documents using unsupervised learning and segregating it into appropriate cluster.
Example, I have 10 topics after running LDA model with the best hyperparameter. So, it should return a number of Topic is already defined withe pre-trained LDA model with new sentence or document that user input.
I am waiting you guys good solution. :)
Ps. I am using Gensim for NLP.
Using Quanteda You can achieve this as follows
dtm <- convert(dfmat_news, to = "topicmodels")
lda <- LDA(dtm, k = 10). #10 topics in this case
Then you can obtain the most likely topics using the command topics() and save them as a document-level variable.
docvars(dfmat_news, 'topic') <- topics(lda)
head(topics(lda), 20)
here the tutorial : https://tutorials.quanteda.io/machine-learning/topicmodel/
hope it is clear and useful :)